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Lecture Notes in Computer Science 9349
Commenced Publication in 1973Founding and Former Series Editors:Gerhard Goos, Juris Hartmanis, and Jan van Leeuwen
Editorial Board
David HutchisonLancaster University, Lancaster, UK
Takeo KanadeCarnegie Mellon University, Pittsburgh, PA, USA
Josef KittlerUniversity of Surrey, Guildford, UK
Jon M. KleinbergCornell University, Ithaca, NY, USA
Friedemann MatternETH Zürich, Zürich, Switzerland
John C. MitchellStanford University, Stanford, CA, USA
Moni NaorWeizmann Institute of Science, Rehovot, Israel
C. Pandu RanganIndian Institute of Technology, Madras, India
Bernhard SteffenTU Dortmund University, Dortmund, Germany
Demetri TerzopoulosUniversity of California, Los Angeles, CA, USA
Doug TygarUniversity of California, Berkeley, CA, USA
Gerhard WeikumMax Planck Institute for Informatics, Saarbrücken, Germany
More information about this series at http://www.springer.com/series/7412
Nassir Navab · Joachim HorneggerWilliam M. Wells · Alejandro F. Frangi (Eds.)
Medical Image Computingand Computer-AssistedIntervention – MICCAI 201518th International ConferenceMunich, Germany, October 5–9, 2015Proceedings, Part I
ABC
EditorsNassir NavabTechnische Universität MünchenGarchingGermany
Joachim HorneggerFriedrich-Alexander-Universität
Erlangen-NürnbergErlangenGermany
William M. WellsBrigham and Women’s HospitalHarvard Medical SchoolBostonUSA
Alejandro F. FrangiUniversity of SheffieldSheffieldUK
ISSN 0302-9743 ISSN 1611-3349 (electronic)Lecture Notes in Computer ScienceISBN 978-3-319-24552-2 ISBN 978-3-319-24553-9 (eBook)DOI 10.1007/978-3-319-24553-9
Library of Congress Control Number: 2015949456
LNCS Sublibrary: SL6 – Image Processing, Computer Vision, Pattern Recognition, and Graphics
Springer Cham Heidelberg New York Dordrecht Londonc© Springer International Publishing Switzerland 2015
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of thematerial is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broad-casting, reproduction on microfilms or in any other physical way, and transmission or information storageand retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now knownor hereafter developed.The use of general descriptive names, registered names, trademarks, service marks, etc. in this publicationdoes not imply, even in the absence of a specific statement, that such names are exempt from the relevantprotective laws and regulations and therefore free for general use.The publisher, the authors and the editors are safe to assume that the advice and information in this bookare believed to be true and accurate at the date of publication. Neither the publisher nor the authors or theeditors give a warranty, express or implied, with respect to the material contained herein or for any errors oromissions that may have been made.
Printed on acid-free paper
Springer International Publishing AG Switzerland is part of Springer Science+Business Media(www.springer.com)
Preface
In 2015, the 18th International Conference on Medical Image Computing andComputer-Assisted Intervention (MICCAI 2015) was held in Munich, Germany.It was organized by the Technical University Munich (TUM) and the FriedrichAlexander University Erlangen-Nuremberg (FAU). The meeting took place inthe Philharmonic Hall “Gasteig” during October 6-8, one week after the world-famous Oktoberfest. Satellite events associated with MICCAI 2015 took placeon October 5 and October 9 in the Holiday Inn Hotel Munich City Centreand Klinikum rechts der Isar. MICCAI 2015 and its satellite events attractedword-leading scientists, engineers, and clinicians, who presented high-standardpapers, aiming at uniting the fields of medical image processing, medical imageformation, and medical robotics.
This year the triple anonymous review process was organized in severalphases. In total, 810 valid submissions were received. The review process washandled by one primary and two secondary Program Committee members foreach paper. It was initiated by the primary Program Committee member, whoassigned a minimum of three expert reviewers. Based on these initial reviews,79 papers were directly accepted and 248 papers were rejected. The remainingpapers went to the rebuttal phase, in which the authors had the chance to re-spond to the concerns raised by reviewers. The reviews and associated rebuttalswere subsequently discussed in the next phase among the reviewers leading tothe acceptance of another 85 papers and the rejection of 118 papers. Subse-quently, secondary Program Committee members issued a recommendation foreach paper by weighing both the reviewers’ recommendations and the authors’rebuttals. This resulted in “accept” for 67 papers and in “reject” for 120 papers.The remaining 92 papers were discussed at a Program Committee meeting inGarching, Germany, in May 2015, where 36 out of 75 Program Committee mem-bers were present. During two days, the 92 papers were examined by expertsin the respective fields resulting in another 32 papers being accepted. In total263 papers of the 810 submitted papers were accepted which corresponds to anacceptance rate of 32.5%.
The frequency of primary and secondary keywords is almost identical in thesubmitted, the rejected, and the accepted paper pools. The top five keywords ofall submissions were machine learning (8.3%), segmentation (7.1%), MRI (6.6%),and CAD (4.9%).
The correlation between the initial keyword counts by category and the ac-cepted papers was 0.98. The correlation with the keyword distribution of therejected papers was 0.99. The distributions of the intermediate accept and re-ject phases was also above 0.9 in all cases, i.e., there was a strong relationshipbetween the submitted paper categories and the finally accepted categories. Thekeyword frequency was essentially not influenced by the review decisions. As a
VI Preface
conclusion, we believe the review process was fair and the distribution of topicsreflects no favor of any particular topic of the conference.
This year we offered all authors the opportunity of presenting their workin a five-minute talk. These talks were organized in 11 parallel sessions settingthe stage for further scientific discussions during the poster sessions of the mainsingle-track conference. Since we consider all of the accepted papers as excellent,the selection of long oral presentations representing different fields in a singletrack was extremely challenging. Therefore, we decided to organize the papers inthese proceedings in a different way than in the conference program. In contrastto the conference program, the proceedings do not differentiate between posterand oral presentations. The proceedings are only organized by conference topics.Only for the sake of the conference program did we decide on oral and posterpresentations. In order to help us in the selection process, we asked the authors tosubmit five-minute short presentations. Based on the five-minute presentationsand the recommendations of the reviewers and Program Committee members,we selected 36 papers for oral presentation. We hope these papers to be to someextent representative of the community covering the entire MICCAI spectrum.The difference in raw review score between the poster and oral presentationswas not statistically significant (p > 0.1). In addition to the oral presentationselection process, all oral presenters were asked to submit their presentationstwo months prior to the conference for review by the Program Committee whochecked the presentations thoroughly and made suggestions for improvement.
Another feature in the conference program is the industry panel that featuresleading members of the medical software and device companies who gave theiropinions and presented their future research directions and their strategies fortranslating scientific observations and results of the MICCAI community intomedical products.
We thank Aslı Okur, who did an excellent job in the preparation of the con-ference. She took part in every detail of the organization for more than one year.We would also like to thank Andreas Maier, who supported Joachim Horneggerin his editorial tasks following his election as president of the Friedrich AlexanderUniversity Erlangen-Nuremberg (FAU) in early 2015. Furthermore, we thank thelocal Organizing Committee for arranging the wonderful venue and the MICCAIStudent Board for organizing the additional student events ranging from a tourto the BMW factory to trips to the world-famous castles of Neuschwanstein andLinderhof. The workshop, challenge, and tutorial chairs did an excellent job inenriching this year’s program. In addition, we thank the MICCAI society forprovision of support and insightful comments as well as the Program Commit-tee for their support during the review process. Last but not least, we thank oursponsors for the financial support that made the conference possible.
We look forward to seeing you in Istanbul, Turkey in 2016!
October 2015 Nassir NavabJoachim HorneggerWilliam M. Wells
Alejandro F. Frangi
Organization
General Chair
Nassir Navab Technische Universitat Munchen, Germany
General Co-chair
Joachim Hornegger Friedrich-Alexander-UniversitatErlangen-Nurnberg, Germany
Program Chairs
Nassir Navab Technische Universitat Munchen, GermanyJoachim Hornegger Friedrich-Alexander-Universitat
Erlangen-Nurnberg, GermanyWilliam M. Wells Harvard Medical School, USAAlejandro F. Frangi University of Sheffield, Sheffield, UK
Local Organization Chairs
Ralf Stauder Technische Universitat Munchen, GermanyAslı Okur Technische Universitat Munchen, GermanyPhilipp Matthies Technische Universitat Munchen, GermanyTobias Zobel Friedrich-Alexander-Universitat
Erlangen-Nurnberg, Germany
Publication Chair
Andreas Maier Friedrich-Alexander-UniversitatErlangen-Nurnberg, Germany
Sponsorship and Publicity Chairs
Stefanie Demirci Technische Universitat Munchen, GermanySu-Lin Lee Imperial College London, UK
VIII Organization
Workshop Chairs
Purang Abolmaesumi University of British Columbia, CanadaWolfgang Wein ImFusion, GermanyBertrand Thirion Inria, FranceNicolas Padoy Universite de Strasbourg, France
Challenge Chairs
Bjorn Menze Technische Universitat Munchen, GermanyLena Maier-Hein German Cancer Research Center, GermanyBram van Ginneken Radboud University, The NetherlandsValeria De Luca ETH Zurich, Switzerland
Tutorial Chairs
Tom Vercauteren University College London, UKTobias Heimann Siemens Corporate Technology, USASonia Pujol Harvard Medical School, USACarlos Alberola University of Valladolid, Spain
MICCAI Society Board of Directors
Stephen Aylward Kitware, Inc., NY, USASimon Duchesne Universite Laval, Quebec, CanadaGabor Fichtinger (Secretary) Queen’s University, Kingston, ON, CanadaAlejandro F. Frangi University of Sheffield, Sheffield, UKPolina Golland MIT, Cambridge, MA, USAPierre Jannin INSERM/INRIA, Rennes, FranceLeo Joskowicz The Hebrew University of JerusalemWiro Niessen
(Executive Director) Erasmus MC - University Medical Centre,Rotterdam, The Netherlands
Nassir Navab Technische Universitat, Munchen, GermanyAlison Noble (President) University of Oxford, Oxford, UKSebastien Ourselin (Treasurer) University College, London, UKXavier Pennec INRIA, Sophia Antipolis, FranceJosien Pluim Eindhoven University of Technology,
The NetherlandsDinggang Shen UNC, Chapel Hill, NC, USALi Shen Indiana University, IN, USA
Organization IX
MICCAI Society Consultants to the Board
Alan Colchester University of Kent, Canterbury, UKTerry Peters University of Western Ontario, London, ON,
CanadaRichard Robb Mayo Clinic College of Medicine, MN, USA
MICCAI Society Staff
Society Secretariat Janette Wallace, CanadaRecording Secretary Jackie Williams, CanadaFellows Nomination
Coordinator Terry Peters, Canada
Program Committee
Acar, BurakBarbu, AdrianBen Ayed, IsmailCastellani, UmbertoCattin, Philippe C.Chung, Albert C.S.Cootes, Timde Bruijne, MarleenDelingette, HerveFahrig, RebeccaFalcao, AlexandreFichtinger, GaborGerig, GuidoGholipour, AliGlocker, BenGreenspan, HayitHager, Gregory D.Hamarneh, GhassanHandels, HeinzHarders, MatthiasHeinrich, Mattias PaulHuang, JunzhouIonasec, RazvanIsgum, IvanaJannin, PierreJoshi, SarangJoskowicz, Leo
Kamen, AliKobashi, SyojiLangs, GeorgLi, ShuoLinguraru, Marius GeorgeLiu, HuafengLu, LeMadabhushi, AnantMaier-Hein, LenaMartel, AnneMasamune, KenMoradi, MehdiNielsen, MadsNielsen, PoulNiethammer, MarcOurselin, SebastienPadoy, NicolasPapademetris, XeniosParagios, NikosPernus, FranjoPohl, KilianPreim, BernhardPrince, JerryRadeva, PetiaRohde, GustavoSabuncu, Mert RorySakuma, Ichiro
X Organization
Salcudean, TimSalvado, OlivierSato, YoshinobuSchnabel, Julia A.Shen, LiStoyanov, DanailStudholme, ColinSyeda-Mahmood, TanveerTaylor, ZeikeUnal, GozdeVan Leemput, Koen
Wassermann, DemianWeese, JurgenWein, WolfgangWu, XiaodongYang, Guang ZhongYap, Pew-ThianYin, ZhaozhengYuan, JingZheng, GuoyanZheng, Yefeng
Additional Reviewers
Abolmaesumi, PurangAchterberg, HakimAcosta-Tamayo, OscarAerts, HugoAfacan, OnurAfsari, BijanAganj, ImanAhad, Md. Atiqur RahmanAhmidi, NargesAichert, AndreAkbari, HamedAkhondi-Asl, AlirezaAksoy, MuratAlam, SaadiaAlberola-Lopez, CarlosAljabar, PaulAllan, MaximilianAllassonnieres, StephanieAntani, SameerAntony, BhavnaArbel, TalAuvray, VincentAwate, SuyashAzzabou, NouraBagci, UlasBai, WenjiaBaka, NoraBalocco, SimoneBao, SiqiBarmpoutis, Angelos
Bartoli, AdrienBatmanghelich, KayhanBaust, MaximilianBaxter, JohnBazin, Pierre-LouisBerger, Marie-OdileBernal, JorgeBernard, OlivierBernardis, ElenaBhatia, KanwalBieth, MarieBilgic, BerkinBirkfellner, WolfgangBlaschko, MatthewBloch, IsabelleBoctor, EmadBogunovic, HrvojeBouarfa, LoubnaBouix, SylvainBourgeat, PierrickBrady, MichaelBrost, AlexanderBuerger, ChristianBurgert, OliverBurschka, DariusCaan, MatthanCahill, NathanCai, WeidongCarass, AaronCardenes, Ruben
Organization XI
Cardoso, Manuel JorgeCarmichael, OwenCaruyer, EmmanuelCathier, PascalCerrolaza, JuanCetin, MustafaCetingul, Hasan ErtanChakravarty, M. MallarChamberland, MaximeChapman, Brian E.Chatelain, PierreChen, GengChen, ShuhangChen, TingCheng, JianCheng, JunCheplygina, VeronikaChicherova, NataliaChowdhury, AnandaChristensen, GaryChui, Chee KongCinar Akakin, HaticeCinquin, PhilippeCiompi, FrancescoClarkson, MatthewClarysse, PatrickCobzas, DanaColliot, OlivierCommowick, OlivierCompas, ColinCorso, JasonCriminisi, AntonioCrum, WilliamCuingnet, RemiDaducci, AlessandroDaga, PankajDalca, AdrianDarkner, SuneDavatzikos, ChristosDawant, BenoitDehghan, EhsanDeligianni, FaniDemirci, StefanieDepeursinge, AdrienDequidt, Jeremie
Descoteaux, MaximeDeslauriers-Gauthier, SamuelDiBella, EdwardDijkstra, JoukeDing, KaiDing, XiaoweiDojat, MichelDong, XiaoDowling, JasonDowson, NicholasDu, JiaDuchateau, NicolasDuchesne, SimonDuncan, James S.Dzyubachyk, OlehEavani, HariniEbrahimi, MehranEhrhardt, JanEklund, AndersEl-Baz, AymanEl-Zehiry, NohaEllis, RandyElson, DanielErdt, MariusErnst, FlorisEslami, AbouzarFallavollita, PascalFang, RuoguFenster, AaronFeragen, AasaFick, RutgerFigl, MichaelFischer, PeterFishbaugh, JamesFletcher, P. ThomasFlorack, LucFonov, VladimirForestier, GermainFradkin, MaximFranz, AlfredFreiman, MotiFreysinger, WolfgangFripp, JurgenFrisch, BenjaminFritscher, Karl
XII Organization
Fundana, KetutGamarnik, ViktorGao, FeiGao, MingchenGao, YaozongGao, YueGaonkar, BilwajGarvin, MonaGaser, ChristianGass, TobiasGatta, CarloGeorgescu, BogdanGerber, SamuelGhesu, Florin-CristianGiannarou, StamatiaGibaud, BernardGibson, EliGilles, BenjaminGinsburg, ShoshanaGirard, GabrielGiusti, AlessandroGoh, AlvinaGoksel, OrcunGoldberger, JacobGolland, PolinaGooya, AliGrady, LeoGray, KatherineGrbic, SasaGrisan, EnricoGrova, ChristopheGubern-Merida, AlbertGuevara, PamelaGuler, Riza AlpGuo, Peifang B.Gur, YanivGutman, BorisGomez, PedroHacihaliloglu, IlkerHaidegger, TamasHajnal, JosephHamamci, AndacHammers, AlexanderHan, DongfengHargreaves, Brian
Hastreiter, PeterHatt, ChuckHawkes, DavidHayasaka, SatoruHaynor, DavidHe, HuiguangHe, TianchengHeckel, FrankHeckemann, RolfHeimann, TobiasHeng, Pheng AnnHennersperger, ChristophHolden, MatthewHong, Byung-WooHonnorat, NicolasHoogendoorn, CorneHossain, BelayatHossain, ShaheraHowe, RobertHu, YipengHuang, HengHuang, XiaojieHuang, XiaoleiHutter, JanaIbragimov, BulatIglesias, Juan EugenioIgual, LauraIordachita, IulianIrving, BenjaminJackowski, MarcelJacob, MathewsJain, AmeetJanoos, FirdausJanowczyk, AndrewJerman, TimJi, ShuiwangJi, SongbaiJiang, HaoJiang, WenchaoJiang, XiJiao, FangxiangJin, YanJolly, Marie-PierreJomier, JulienJoshi, Anand
Organization XIII
Joshi, ShantanuJung, ClaudioK.B., JayanthiKabus, SvenKadoury, SamuelKahl, FredrikKainmueller, DagmarKainz, BernhardKakadiaris, IoannisKandemir, MelihKapoor, AnkurKapur, TinaKatouzian, AminKelm, MichaelKerrien, ErwanKersten-Oertel, MartaKhan, AliKhurd, ParmeshwarKiaii, BobKikinis, RonKim, BoklyeKim, EdwardKim, MinjeongKim, SungminKing, AndrewKlein, StefanKlinder, TobiasKluckner, StefanKobayahsi, EtsukoKonukoglu, EnderKumar, PuneetKunz, ManuelaKohler, ThomasLadikos, AlexanderLandman, BennettLang, AndrewLapeer, RudyLarrabide, IgnacioLasser, TobiasLauze, FrancoisLay, NathanLe Reste, Pierre-JeanLee, Han SangLee, KangjooLee, Su-Lin
Lefkimmiatis, StamatisLefevre, JulienLekadir, KarimLelieveldt, BoudewijnLenglet, ChristopheLepore, NatashaLesage, DavidLi, GangLi, JiangLi, QuanzhengLi, XiangLi, YangLi, YeqingLiang, LiangLiao, HongenLin, HenryLindeman, RobertLindner, ClaudiaLinte, CristianLitjens, GeertLiu, FengLiu, JiaminLiu, Jian FeiLiu, JundongLiu, MingxiaLiu, SidongLiu, TianmingLiu, TingLiu, YinxiaoLombaert, HerveLorenz, CristianLorenzi, MarcoLou, XinghuaLu, YaoLuo, XiongbiaoLv, JingleiLuthi, MarcelMaass, NicoleMadooei, AliMahapatra, DwarikanathMaier, AndreasMaier-Hein (ne Fritzsche),
Klaus HermannMajumdar, AngshulMalandain, Gregoire
XIV Organization
Mansi, TommasoMansoor, AwaisMao, HongdaMao, YunxiangMarchesseau, StephanieMargeta, JanMarini Silva, RafaelMariottini, Gian LucaMarsden, AlisonMarsland, StephenMartin-Fernandez, MarcosMartı, RobertMasutani, YoshitakaMateus, DianaMcClelland, JamieMcIntosh, ChrisMedrano-Gracia, PauMeier, RaphaelMendizabal-Ruiz, E. GerardoMenegaz, GloriaMenze, BjoernMeyer, ChuckMiga, MichaelMihalef, ViorelMiller, JamesMiller, KarolMisaki, MasayaModat, MarcMoghari, MehdiMohamed, AshrafMohareri, OmidMoore, JohnMorales, Hernan G.Moreno, RodrigoMori, KensakuMorimoto, MasakazuMurphy, KeelinMuller, HenningNabavi, AryaNakajima, YoshikazuNakamura, RyoichiNapel, SandyNappi, JanneNasiriavanaki, MohammadrezaNenning, Karl-Heinz
Neumann, DominikNeumuth, ThomasNg, BernardNguyen Van, HienNicolau, stephaneNing, LipengNoble, AlisonNoble, JackNoblet, VincentO’Donnell, LaurenO’Donnell, ThomasOda, MasahiroOeltze-Jafra, SteffenOh, JunghwanOktay, Ayse BetulOkur, AslıOliver, ArnauOlivetti, EmanueleOnofrey, JohnOnogi, ShinyaOrihuela-Espina, FelipeOtake, YoshitoOu, YangmingOzarslan, EvrenPace, DaniellePapa, JoaoParsopoulos, KonstantinosPaul, PerrinePaulsen, RasmusPeng, TingyingPennec, XavierPeruzzo, DenisPeter, LoicPeterlik, IgorPetersen, JensPetitjean, CarolinePeyrat, Jean-MarcPham, DzungPiella, GemmaPitiot, AlainPizzolato, MarcoPlenge, EsbenPluim, JosienPoline, Jean-BaptistePrasad, Gautam
Organization XV
Prastawa, MarcelPratt, PhilipPreiswerk, FrankPreusser, TobiasPrevost, RaphaelPrieto, ClaudiaPunithakumar, KumaradevanPutzer, DavidQian, XiaoningQiu, WuQuellec, GwenoleRafii-Tari, HedyehRajchl, MartinRajpoot, NasirRaniga, ParneshRapaka, SaikiranRathi, YogeshRathke, FabianRauber, PauloReinertsen, IngeridReinhardt, JosephReiter, AustinRekik, IslemReyes, MauricioRicha, RogerioRieke, NicolaRiess, ChristianRiklin Raviv, TammyRisser, LaurentRit, SimonRivaz, HassanRobinson, EmmaRoche, AlexisRohling, RobertRohr, KarlRopinski, TimoRoth, HolgerRousseau, FrancoisRousson, MikaelRoy, SnehashisRueckert, DanielRueda Olarte, AndreaRuijters, DanielSamaras, DimitrisSarry, Laurent
Sato, JoaoSchaap, MichielScheinost, DustinScherrer, BenoitSchirmer, Markus D.Schmidt, FrankSchmidt-Richberg, AlexanderSchneider, CaitlinSchneider, MatthiasSchultz, ThomasSchumann, SteffenSchwartz, ErnstSechopoulos, IoannisSeiler, ChristofSeitel, AlexanderSermesant, MaximeSeshamani, SharmishtaaShahzad, RahilShamir, Reuben R.Sharma, PuneetShen, XilinShi, FengShi, KuangyuShi, WenzheShi, YinghuanShi, YonggangShin, Hoo-ChangSimpson, AmberSingh, VikasSinkus, RalphSlabaugh, GregSmeets, DirkSona, DiegoSong, YangSotiras, AristeidisSpeidel, MichaelSpeidel, StefanieSpiclin, ZigaStaib, LawrenceStamm, AymericStaring, MariusStauder, RalfStayman, J. WebsterSteinman, DavidStewart, James
XVI Organization
Styles, IainStyner, MartinSu, HangSuinesiaputra, AvanSuk, Heung-IlSummers, RonaldSun, ShanhuiSzekely, GaborSznitman, RaphaelTakeda, TakahiroTalbot, HuguesTam, RogerTamura, ManabuTang, LisaTao, LinglingTasdizen, TolgaTaylor, RussellThirion, BertrandThung, Kim-HanTiwari, PallaviToews, MatthewToh, Kim-ChuanTokuda, JunichiTong, YubingTornai, Gabor JanosTosun, DuyguTotz, JohannesTournier, J-DonaldToussaint, NicolasTraub, JoergTroccaz, JocelyneTustison, NicholasTwinanda, Andru PutraTwining, CaroleUkwatta, ErangaUmadevi Venkataraju, KannanUnay, DevrimUrschler, MartinUzunbas, MustafaVaillant, RegisVallin Spina, ThiagoVallotton, PascalVan assen, HansVan Ginneken, BramVan Tulder, Gijs
Van Walsum, TheoVandini, AlessandroVannier, MichaelVaroquaux, GaelVegas-Sanchez-Ferrero, GonzaloVenkataraman, ArchanaVercauteren, TomVeta, MtikoVignon-Clementel, IreneVillard, Pierre-FredericVisentini-Scarzanella, MarcoViswanath, SatishVitanovski, DimeVoigt, IngmarVon Berg, JensVoros, SandrineVrtovec, TomazWachinger, ChristianWaechter-Stehle, IrinaWahle, AndreasWang, AncongWang, ChaohuiWang, HaiboWang, HongzhiWang, JunchenWang, LiWang, LianshengWang, LichaoWang, LinweiWang, QianWang, QiuWang, SongWang, YalinWang, YuanquanWee, Chong-YawWei, LiuWels, MichaelWerner, ReneWesarg, StefanWestin, Carl-FredrikWhitaker, RossWhitney, JonWiles, AndrewWorz, StefanWu, Guorong
Organization XVII
Wu, HaiyongWu, Yu-ChienXie, YuchenXing, FuyongXu, YanwuXu, ZiyueXue, ZhongYagi, NaomiYamazaki, TakaharuYan, PingkunYang, LinYang, YingYao, JianhuaYaqub, MohammadYe, Dong HyeYeo, B.T. ThomasYnnermann, AndersYoung, AlistairYushkevich, PaulZacharaki, EvangeliaZacur, ErnestoZelmann, RinaZeng, WeiZhan, Liang
Zhan, YiqiangZhang, DaoqiangZhang, HuiZhang, LingZhang, MiaomiaoZhang, PeiZhang, ShaotingZhang, TianhaoZhang, TuoZhang, YongZhao, BoZhao, WeiZhijun, ZhangZhou, jinghaoZhou, LupingZhou, S. KevinZhou, YanZhu, DajiangZhu, HongtuZhu, YueminZhuang, lingZhuang, XiahaiZuluaga, Maria A.
Contents – Part I
Advanced MRI: Diffusion, fMRI, DCE
Automatic Segmentation of Renal Compartmentsin DCE-MRI Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Xin Yang, Hung Le Minh, Tim Cheng, Kyung Hyun Sung,and Wenyu Liu
Harmonizing Diffusion MRI Data Across Multiple Sites and Scanners . . . 12Hengameh Mirzaalian, Amicie de Pierrefeu, Peter Savadjiev,Ofer Pasternak, Sylvain Bouix, Marek Kubicki, Carl-Fredrik Westin,Martha E. Shenton, and Yogesh Rathi
Track Filtering via Iterative Correction of TDI Topology . . . . . . . . . . . . . . 20Dogu Baran Aydogan and Yonggang Shi
Novel Single and Multiple Shell Uniform Sampling Schemes forDiffusion MRI Using Spherical Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Jian Cheng, Dinggang Shen, Pew-Thian Yap, and Peter J. Basser
q-Space Deep Learning for Twelve-Fold Shorter and Model-FreeDiffusion MRI Scans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
Vladimir Golkov, Alexey Dosovitskiy, Philipp Samann,Jonathan I. Sperl, Tim Sprenger, Michael Czisch, Marion I. Menzel,Pedro A. Gomez, Axel Haase, Thomas Brox, and Daniel Cremers
A Machine Learning Based Approach to Fiber Tractography UsingClassifier Voting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
Peter F. Neher, Michael Gotz, Tobias Norajitra, Christian Weber,and Klaus H. Maier-Hein
Symmetric Wiener Processes for Probabilistic Tractography andConnectivity Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
M. Reisert, B. Dihtal, E. Kellner, H. Skibbe, and C. Kaller
An Iterated Complex Matrix Approach for Simulation and Analysis ofDiffusion MRI Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
Hans Knutsson, Magnus Herberthson, and Carl-Fredrik Westin
Prediction of Motor Function in Very Preterm Infants UsingConnectome Features and Local Synthetic Instances . . . . . . . . . . . . . . . . . . 69
Colin J. Brown, Steven P. Miller, Brian G. Booth,Kenneth J. Poskitt, Vann Chau, Anne R. Synnes,Jill G. Zwicker, Ruth E. Grunau, and Ghassan Hamarneh
XX Contents – Part I
Segmenting Kidney DCE-MRI Using 1st-Order Shape and 5th-OrderAppearance Priors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Ni Liu, Ahmed Soliman, Georgy Gimel’farb, and Ayman El-Baz
Comparison of Stochastic and Variational Solutions to ASL fMRI DataAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Aina Frau-Pascual, Florence Forbes, and Philippe Ciuciu
Prediction of CT Substitutes from MR Images Based on Local SparseCorrespondence Combination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Yao Wu, Wei Yang, Lijun Lu, Zhentai Lu, Liming Zhong, Ru Yang,Meiyan Huang, Yanqiu Feng, Wufan Chen, and Qianjin Feng
Robust Automated White Matter Pathway Reconstruction for LargeStudies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Jalmar Teeuw, Matthan W.A. Caan, and Silvia D. Olabarriaga
Exploiting the Phase in Diffusion MRI for Microstructure Recovery:Towards Axonal Tortuosity via Asymmetric Diffusion Processes . . . . . . . . 109
Marco Pizzolato, Demian Wassermann, Timothe Boutelier,and Rachid Deriche
Sparse Bayesian Inference of White Matter Fiber Orientations fromCompressed Multi-resolution Diffusion MRI . . . . . . . . . . . . . . . . . . . . . . . . . 117
Pramod Kumar Pisharady, Julio M. Duarte-Carvajalino,Stamatios N. Sotiropoulos, Guillermo Sapiro, and Christophe Lenglet
Classification of MRI under the Presence of Disease HeterogeneityUsing Multi-Task Learning: Application to Bipolar Disorder . . . . . . . . . . . 125
Xiangyang Wang, Tianhao Zhang, Tiffany M. Chaim,Marcus V. Zanetti, and Christos Davatzikos
Fiber Connection Pattern-Guided Structured Sparse Representation ofWhole-Brain fMRI Signals for Functional Network Inference . . . . . . . . . . . 133
Xi Jiang, Tuo Zhang, Qinghua Zhao, Jianfeng Lu, Lei Guo,and Tianming Liu
Joint Estimation of Hemodynamic Response Function and VoxelActivation in Functional MRI Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
Priya Aggarwal, Anubha Gupta, and Ajay Garg
Quantifying Microstructure in Fiber Crossings with DiffusionalKurtosis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
Michael Ankele and Thomas Schultz
Which Manifold Should be Used for Group Comparison in DiffusionTensor Imaging? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
A. Bouchon, V. Noblet, F. Heitz, J. Lamy, F. Blanc,and J.-P. Armspach
Contents – Part I XXI
Convex Non-negative Spherical Factorization of Multi-shellDiffusion-Weighted Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
Daan Christiaens, Frederik Maes, Stefan Sunaert, and Paul Suetens
Tensorial Spherical Polar Fourier Diffusion MRI with OptimalDictionary Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
Jian Cheng, Dinggang Shen, Pew-Thian Yap, and Peter J. Basser
Diffusion Compartmentalization Using Response Function Groups withCardinality Penalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
Pew-Thian Yap, Yong Zhang, and Dinggang Shen
V–Bundles: Clustering Fiber Trajectories from Diffusion MRI in LinearTime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
Andre Reichenbach, Mathias Goldau, Christian Heine,and Mario Hlawitschka
Assessment of Mean Apparent Propagator-Based Indices as Biomarkersof Axonal Remodeling after Stroke . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Lorenza Brusini, Silvia Obertino, Mauro Zucchelli,Ilaria Boscolo Galazzo, Gunnar Krueger, Cristina Granziera,and Gloria Menegaz
Integrating Multimodal Priors in Predictive Models for the FunctionalCharacterization of Alzheimer’s Disease . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
Mehdi Rahim, Bertrand Thirion, Alexandre Abraham,Michael Eickenberg, Elvis Dohmatob, Claude Comtat,and Gael Varoquaux
Simultaneous Denoising and Registration for Accurate CardiacDiffusion Tensor Reconstruction from MRI . . . . . . . . . . . . . . . . . . . . . . . . . . 215
Valeriy Vishnevskiy, Christian Stoeck, Gabor Szekely,Christine Tanner, and Sebastian Kozerke
Iterative Subspace Screening for Rapid Sparse Estimation of BrainTissue Microstructural Properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223
Pew-Thian Yap, Yong Zhang, and Dinggang Shen
Elucidating Intravoxel Geometry in Diffusion-MRI: AsymmetricOrientation Distribution Functions (AODFs) Revealedby a Cone Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231
Suheyla Cetin, Evren Ozarslan, and Gozde Unal
Modeling Task FMRI Data via Supervised Stochastic CoordinateCoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
Jinglei Lv, Binbin Lin, Wei Zhang, Xi Jiang, Xintao Hu,Junwei Han, Lei Guo, Jieping Ye, and Tianming Liu
XXII Contents – Part I
Computer Assisted and Image-Guided Interventions
Autonomous Ultrasound-Guided Tissue Dissection . . . . . . . . . . . . . . . . . . . 249Philip Pratt, Archie Hughes-Hallett, Lin Zhang, Nisha Patel,Erik Mayer, Ara Darzi, and Guang-Zhong Yang
A Compact Retinal-Surgery Telemanipulator that Uses DisposableInstruments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 258
Manikantan Nambi, Paul S. Bernstein, and Jake J. Abbott
Surgical Tool Tracking and Pose Estimation in Retinal Microsurgery . . . 266Nicola Rieke, David Joseph Tan, Mohamed Alsheakhali,Federico Tombari, Chiara Amat di San Filippo,Vasileios Belagiannis, Abouzar Eslami, and Nassir Navab
Direct Calibration of a Laser Ablation System in the Projective VoltageSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
Adrian Schneider, Simon Pezold, Kyung-won Baek, Dilyan Marinov,and Philippe C. Cattin
Surrogate-Driven Estimation of Respiratory Motion and Layers inX-Ray Fluoroscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
Peter Fischer, Thomas Pohl, Andreas Maier, and Joachim Hornegger
Robust 5DOF Transesophageal Echo Probe Tracking at FluoroscopicFrame Rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
Charles R. Hatt, Michael A. Speidel, and Amish N. Raval
Rigid Motion Compensation in Interventional C-arm CT UsingConsistency Measure on Projection Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
Robert Frysch and Georg Rose
Hough Forests for Real-Time, Automatic Device Localization inFluoroscopic Images: Application to TAVR . . . . . . . . . . . . . . . . . . . . . . . . . . 307
Charles R. Hatt, Michael A. Speidel, and Amish N. Raval
Hybrid Utrasound and MRI Acquisitions for High-Speed Imagingof Respiratory Organ Motion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 315
Frank Preiswerk, Matthew Toews, W. Scott Hoge,Jr-yuan George Chiou, Lawrence P. Panych,William M. Wells III, and Bruno Madore
A Portable Intra-Operative Framework Applied to Distal RadiusFracture Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 323
Jessica Magaraggia, Wei Wei, Markus Weiten, Gerhard Kleinszig,Sven Vetter, Jochen Franke, Karl Barth, Elli Angelopoulou,and Joachim Hornegger
Contents – Part I XXIII
Image Based Surgical Instrument Pose Estimation with Multi-classLabelling and Optical Flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
Max Allan, Ping-Lin Chang, Sebastien Ourselin, David J. Hawkes,Ashwin Sridhar, John Kelly, and Danail Stoyanov
Adaption of 3D Models to 2D X-Ray Images during EndovascularAbdominal Aneurysm Repair . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339
Daniel Toth, Marcus Pfister, Andreas Maier, Markus Kowarschik,and Joachim Hornegger
Projection-Based Phase Features for Localization of a Needle Tip in 2DCurvilinear Ultrasound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347
Ilker Hacihaliloglu, Parmida Beigi, Gary Ng, Robert N. Rohling,Septimiu Salcudean, and Purang Abolmaesumi
Inertial Measurement Unit for Radiation-Free Navigated ScrewPlacement in Slipped Capital Femoral Epiphysis Surgery . . . . . . . . . . . . . . 355
Bamshad Azizi Koutenaei, Ozgur Guler, Emmanuel Wilson,Matthew Oetgen, Patrick Grimm, Nassir Navab, and Kevin Cleary
Pictorial Structures on RGB-D Images for Human Pose Estimation inthe Operating Room . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
Abdolrahim Kadkhodamohammadi, Afshin Gangi,Michel de Mathelin, and Nicolas Padoy
Interventional Photoacoustic Imaging of the Human Placenta withUltrasonic Tracking for Minimally Invasive Fetal Surgeries . . . . . . . . . . . . 371
Wenfeng Xia, Efthymios Maneas, Daniil I. Nikitichev,Charles A. Mosse, Gustavo Sato dos Santos, Tom Vercauteren,Anna L. David, Jan Deprest, Sebastien Ourselin, Paul C. Beard,and Adrien E. Desjardins
Automated Segmentation of Surgical Motion for Performance Analysisand Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379
Yun Zhou, Ioanna Ioannou, Sudanthi Wijewickrema, James Bailey,Gregor Kennedy, and Stephen O’Leary
Vision-Based Intraoperative Cone-Beam CT Stitching forNon-overlapping Volumes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387
Bernhard Fuerst, Javad Fotouhi, and Nassir Navab
Visibility Map: A New Method in Evaluation Quality of OpticalColonoscopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 396
Mohammad Ali Armin, Hans De Visser, Girija Chetty,Cedric Dumas, David Conlan, Florian Grimpen, and Olivier Salvado
Tissue Surface Reconstruction Aided by Local Normal InformationUsing a Self-calibrated Endoscopic Structured Light System . . . . . . . . . . . 405
Jianyu Lin, Neil T. Clancy, Danail Stoyanov, and Daniel S. Elson
XXIV Contents – Part I
Surgical Augmented Reality with Topological Changes . . . . . . . . . . . . . . . . 413Christoph J. Paulus, Nazim Haouchine, David Cazier,and Stephane Cotin
Towards an Efficient Computational Framework for Guiding SurgicalResection through Intra-operative Endo-microscopic Pathology . . . . . . . . 421
Shaohua Wan, Shanhui Sun, Subhabrata Bhattacharya,Stefan Kluckner, Alexander Gigler, Elfriede Simon,Maximilian Fleischer, Patra Charalampaki, Terrence Chen,and Ali Kamen
Automated Assessment of Surgical Skills Using Frequency Analysis . . . . . 430Aneeq Zia, Yachna Sharma, Vinay Bettadapura, Eric L. Sarin,Mark A. Clements, and Irfan Essa
Robust Live Tracking of Mitral Valve Annulus for Minimally-InvasiveIntervention Guidance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 439
Ingmar Voigt, Mihai Scutaru, Tommaso Mansi, Bogdan Georgescu,Noha El-Zehiry, Helene Houle, and Dorin Comaniciu
Motion-Aware Mosaicing for Confocal Laser Endomicroscopy . . . . . . . . . . 447Jessie Mahe, Nicolas Linard, Marzieh Kohandani Tafreshi,Tom Vercauteren, Nicholas Ayache, Francois Lacombe,and Remi Cuingnet
A Registration Approach to Endoscopic Laser Speckle ContrastImaging for Intrauterine Visualisation of Placental Vessels . . . . . . . . . . . . . 455
Gustavo Sato dos Santos, Efthymios Maneas, Daniil Nikitichev,Anamaria Barburas, Anna L. David, Jan Deprest,Adrien Desjardins, Tom Vercauteren, and Sebastien Ourselin
Marker-Less AR in the Hybrid Room Using Equipment Detection forCamera Relocalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 463
Nicolas Loy Rodas, Fernando Barrera, and Nicolas Padoy
Hybrid Retargeting for High-Speed Targeted Optical Biopsies . . . . . . . . . 471Andre Mouton, Menglong Ye, Francois Lacombe,and Guang-Zhong Yang
Visual Force Feedback for Hand-Held Microsurgical Instruments . . . . . . . 480Gauthier Gras, Hani J. Marcus, Christopher J. Payne, Philip Pratt,and Guang-Zhong Yang
Automated Three-Piece Digital Dental Articulation . . . . . . . . . . . . . . . . . . 488Jianfu Li, Flavio Ferraz, Shunyao Shen, Yi-Fang Lo,Xiaoyan Zhang, Peng Yuan, Zhen Tang, Ken-Chung Chen,Jaime Gateno, Xiaobo Zhou, and James J. Xia
Contents – Part I XXV
A System for MR-Ultrasound Guidance during Robot-AssistedLaparoscopic Radical Prostatectomy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497
Omid Mohareri, Guy Nir, Julio Lobo, Richard Savdie, Peter Black,and Septimiu Salcudean
Computer Aided Diagnosis I: Machine Learning
Automatic Fetal Ultrasound Standard Plane Detection UsingKnowledge Transferred Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . 507
Hao Chen, Qi Dou, Dong Ni, Jie-Zhi Cheng, Jing Qin, Shengli Li,and Pheng-Ann Heng
Automatic Localization and Identification of Vertebrae in Spine CT viaa Joint Learning Model with Deep Neural Networks . . . . . . . . . . . . . . . . . . 515
Hao Chen, Chiyao Shen, Jing Qin, Dong Ni, Lin Shi,Jack C.Y. Cheng, and Pheng-Ann Heng
Identification of Cerebral Small Vessel Disease Using Multiple InstanceLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 523
Liang Chen, Tong Tong, Chin Pang Ho, Rajiv Patel, David Cohen,Angela C. Dawson, Omid Halse, Olivia Geraghty, Paul E.M. Rinne,Christopher J. White, Tagore Nakornchai, Paul Bentley,and Daniel Rueckert
Why Does Synthesized Data Improve Multi-sequence Classification? . . . . 531Gijs van Tulder and Marleen de Bruijne
Label Stability in Multiple Instance Learning . . . . . . . . . . . . . . . . . . . . . . . . 539Veronika Cheplygina, Lauge Sørensen, David M.J. Tax,Marleen de Bruijne, and Marco Loog
Spectral Forests: Learning of Surface Data, Application to CorticalParcellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 547
Herve Lombaert, Antonio Criminisi, and Nicholas Ayache
DeepOrgan: Multi-level Deep Convolutional Networks for AutomatedPancreas Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 556
Holger R. Roth, Le Lu, Amal Farag, Hoo-Chang Shin, Jiamin Liu,Evrim B. Turkbey, and Ronald M. Summers
3D Deep Learning for Efficient and Robust Landmark Detection inVolumetric Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 565
Yefeng Zheng, David Liu, Bogdan Georgescu, Hien Nguyen,and Dorin Comaniciu
A Hybrid of Deep Network and Hidden Markov Model for MCIIdentification with Resting-State fMRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573
Heung-Il Suk, Seong-Whan Lee, and Dinggang Shen
XXVI Contents – Part I
Combining Unsupervised Feature Learning and Riesz Wavelets forHistopathology Image Representation: Application to IdentifyingAnaplastic Medulloblastoma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 581
Sebastian Otalora, Angel Cruz-Roa, John Arevalo, Manfredo Atzori,Anant Madabhushi, Alexander R. Judkins, Fabio Gonzalez,Henning Muller, and Adrien Depeursinge
Automatic Coronary Calcium Scoring in Cardiac CT AngiographyUsing Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589
Jelmer M. Wolterink, Tim Leiner, Max A. Viergever,and Ivana Isgum
A Random Riemannian Metric for Probabilistic Shortest-PathTractography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597
Søren Hauberg, Michael Schober, Matthew Liptrot, Philipp Hennig,and Aasa Feragen
Deep Learning and Structured Prediction for the Segmentation of Massin Mammograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 605
Neeraj Dhungel, Gustavo Carneiro, and Andrew P. Bradley
Learning Tensor-Based Features for Whole-Brain fMRI Classification . . . 613Xiaonan Song, Lingnan Meng, Qiquan Shi, and Haiping Lu
Prediction of Trabecular Bone Anisotropy from Quantitative ComputedTomography Using Supervised Learning and a Novel MorphometricFeature Descriptor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621
Vimal Chandran, Philippe Zysset, and Mauricio Reyes
Automatic Diagnosis of Ovarian Carcinomas via Sparse MultiresolutionTissue Representation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 629
Aıcha BenTaieb, Hector Li-Chang, David Huntsman,and Ghassan Hamarneh
Scale-Adaptive Forest Training via an Efficient Feature SamplingScheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 637
Loıc Peter, Olivier Pauly, Pierre Chatelain, Diana Mateus,and Nassir Navab
Multiple Instance Cancer Detection by Boosting Regularised Trees . . . . . 645Wenqi Li, Jianguo Zhang, and Stephen J. McKenna
Uncertainty-Driven Forest Predictors for Vertebra Localization andSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 653
David Richmond, Dagmar Kainmueller, Ben Glocker,Carsten Rother, and Gene Myers
Who Is Talking to Whom: Synaptic Partner Detection in AnisotropicVolumes of Insect Brain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 661
Anna Kreshuk, Jan Funke, Albert Cardona, and Fred A. Hamprecht
Contents – Part I XXVII
Direct and Simultaneous Four-Chamber Volume Estimation byMulti-Output Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669
Xiantong Zhen, Ali Islam, Mousumi Bhaduri, Ian Chan, and Shuo Li
Cross-Domain Synthesis of Medical Images Using EfficientLocation-Sensitive Deep Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 677
Hien Van Nguyen, Kevin Zhou, and Raviteja Vemulapalli
Grouping Total Variation and Sparsity: Statistical Learning withSegmenting Penalties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 685
Michael Eickenberg, Elvis Dohmatob, Bertrand Thirion,and Gael Varoquaux
Scandent Tree: A Random Forest Learning Method for IncompleteMultimodal Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 694
Soheil Hor and Mehdi Moradi
Disentangling Disease Heterogeneity with Max-Margin MultipleHyperplane Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702
Erdem Varol, Aristeidis Sotiras, and Christos Davatzikos
Marginal Space Deep Learning: Efficient Architecture for Detection inVolumetric Image Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 710
Florin C. Ghesu, Bogdan Georgescu, Yefeng Zheng,Joachim Hornegger, and Dorin Comaniciu
Nonlinear Regression on Riemannian Manifolds and Its Applications toNeuro-Image Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719
Monami Banerjee, Rudrasis Chakraborty, Edward Ofori,David Vaillancourt, and Baba C. Vemuri
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 729
Contents – Part II
Computer Aided Diagnosis II: Automation
Computer-Aided Detection and Quantification of IntracranialAneurysms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Tim Jerman, Franjo Pernus, Bostjan Likar, and Ziga Spiclin
Discriminative Feature Selection for Multiple Ocular DiseasesClassification by Sparse Induced Graph Regularized Group Lasso . . . . . . 11
Xiangyu Chen, Yanwu Xu, Shuicheng Yan, Tat-Seng Chua,Damon Wing Kee Wong, Tien Yin Wong, and Jiang Liu
Detection of Glands and Villi by Collaboration of Domain Knowledgeand Deep Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
Jiazhuo Wang, John D. MacKenzie, Rageshree Ramachandran,and Danny Z. Chen
Minimum S-Excess Graph for Segmenting and Tracking MultipleBorders with HMM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
Ehab Essa, Xianghua Xie, and Jonathan-Lee Jones
Automatic Artery-Vein Separation from Thoracic CT Images UsingInteger Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Christian Payer, Michael Pienn, Zoltan Balint, Andrea Olschewski,Horst Olschewski, and Martin Urschler
Automatic Dual-View Mass Detection in Full-Field DigitalMammograms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Guy Amit, Sharbell Hashoul, Pavel Kisilev, Boaz Ophir,Eugene Walach, and Aviad Zlotnick
Leveraging Mid-Level Semantic Boundary Cues for Automated LymphNode Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
Ari Seff, Le Lu, Adrian Barbu, Holger Roth, Hoo-Chang Shin,and Ronald M. Summers
Computer-Aided Pulmonary Embolism Detection Using a NovelVessel-Aligned Multi-planar Image Representation and ConvolutionalNeural Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Nima Tajbakhsh, Michael B. Gotway, and Jianming Liang
XXX Contents – Part II
Ultrasound-Based Detection of Prostate Cancer Using AutomaticFeature Selection with Deep Belief Networks . . . . . . . . . . . . . . . . . . . . . . . . 70
Shekoofeh Azizi, Farhad Imani, Bo Zhuang, Amir Tahmasebi,Jin Tae Kwak, Sheng Xu, Nishant Uniyal, Baris Turkbey,Peter Choyke, Peter Pinto, Bradford Wood, Mehdi Moradi,Parvin Mousavi, and Purang Abolmaesumi
MCI Identification by Joint Learning on Multiple MRI Data . . . . . . . . . . . 78Yue Gao, Chong-Yaw Wee, Minjeong Kim,Panteleimon Giannakopoulos, Marie-Louise Montandon,Sven Haller, and Dinggang Shen
Medical Image Retrieval Using Multi-graph Learning for MCIDiagnostic Assistance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
Yue Gao, Ehsan Adeli-M., Minjeong Kim,Panteleimon Giannakopoulos, Sven Haller,and Dinggang Shen
Regenerative Random Forest with Automatic Feature Selection toDetect Mitosis in Histopathological Breast Cancer Images . . . . . . . . . . . . . 94
Angshuman Paul, Anisha Dey, Dipti Prasad Mukherjee,Jayanthi Sivaswamy, and Vijaya Tourani
HoTPiG: A Novel Geometrical Feature for Vessel Morphometry and ItsApplication to Cerebral Aneurysm Detection . . . . . . . . . . . . . . . . . . . . . . . . 103
Shouhei Hanaoka, Yukihiro Nomura, Mitsutaka Nemoto,Soichiro Miki, Takeharu Yoshikawa, Naoto Hayashi, Kuni Ohtomo,Yoshitaka Masutani, and Akinobu Shimizu
Robust Segmentation of Various Anatomies in 3D Ultrasound UsingHough Forests and Learned Data Representations . . . . . . . . . . . . . . . . . . . . 111
Fausto Milletari, Seyed-Ahmad Ahmadi, Christine Kroll,Christoph Hennersperger, Federico Tombari, Amit Shah,Annika Plate, Kai Boetzel, and Nassir Navab
Improved Parkinson’s Disease Classification from Diffusion MRI Databy Fisher Vector Descriptors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Luis Salamanca, Nikos Vlassis, Nico Diederich, Florian Bernard,and Alexander Skupin
Automated Shape and Texture Analysis for Detection of Osteoarthritisfrom Radiographs of the Knee . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Jessie Thomson, Terence O’Neill, David Felson, and Tim Cootes
Hashing Forests for Morphological Search and Retrieval inNeuroscientific Image Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
Sepideh Mesbah, Sailesh Conjeti, Ajayrama Kumaraswamy,Philipp Rautenberg, Nassir Navab, and Amin Katouzian
Contents – Part II XXXI
Computer-Aided Infarction Identification from Cardiac CT Images:A Biomechanical Approach with SVM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
Ken C.L. Wong, Michael Tee, Marcus Chen, David A. Bluemke,Ronald M. Summers, and Jianhua Yao
Automatic Graph-Based Localization of Cochlear Implant Electrodesin CT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
Jack H. Noble and Benoit M. Dawant
Towards Non-invasive Image-Based Early Diagnosis of Autism . . . . . . . . . 160M. Mostapha, M.F. Casanova, G. Gimel’farb, and A. El-Baz
Identifying Connectome Module Patterns via New BalancedMulti-graph Normalized Cut . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
Hongchang Gao, Chengtao Cai, Jingwen Yan, Lin Yan,Joaquin Goni Cortes, Yang Wang, Feiping Nie, John West,Andrew Saykin, Li Shen, and Heng Huang
Semantic 3-D Labeling of Ear Implants Using a Global ParametricTransition Prior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Alexander Zouhar, Carsten Rother, and Siegfried Fuchs
Learning the Correlation Between Images and Disease Labels UsingAmbiguous Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Tanveer Syeda-Mahmood, Ritwik Kumar, and Colin Compas
Longitudinal Analysis of Brain Recovery after Mild Traumatic BrainInjury Based on Groupwise Consistent Brain Network Clusters . . . . . . . . 194
Hanbo Chen, Armin Iraji, Xi Jiang, Jinglei Lv, Zhifeng Kou,and Tianming Liu
Registration: Method and Advanced Applications
Dictionary Learning Based Image Descriptor for MyocardialRegistration of CP-BOLD MR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205
Ilkay Oksuz, Anirban Mukhopadhyay, Marco Bevilacqua,Rohan Dharmakumar, and Sotirios A. Tsaftaris
Registration of Color and OCT Fundus Images Using Low-dimensionalStep Pattern Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
Jimmy Addison Lee, Jun Cheng, Guozhen Xu, Ee Ping Ong,Beng Hai Lee, Damon Wing Kee Wong, and Jiang Liu
Estimating Patient Specific Templates for Pre-operative and Follow-UpBrain Tumor Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
Dongjin Kwon, Ke Zeng, Michel Bilello, and Christos Davatzikos
XXXII Contents – Part II
Topography-Based Registration of Developing Cortical Surfaces inInfants Using Multidirectional Varifold Representation . . . . . . . . . . . . . . . . 230
Islem Rekik, Gang Li, Weili Lin, and Dinggang Shen
A Liver Atlas Using the Special Euclidean Group . . . . . . . . . . . . . . . . . . . . 238Mohamed S. Hefny, Toshiyuki Okada, Masatoshi Hori,Yoshinobu Sato, and Randy E. Ellis
Multi-scale and Multimodal Fusion of Tract-Tracing, Myelin Stain andDTI-derived Fibers in Macaque Brains . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246
Tuo Zhang, Jun Kong, Ke Jing, Hanbo Chen, Xi Jiang,Longchuan Li, Lei Guo, Jianfeng Lu, Xiaoping Hu,and Tianming Liu
Space-Frequency Detail-Preserving Construction of Neonatal BrainAtlases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255
Yuyao Zhang, Feng Shi, Pew-Thian Yap, and Dinggang Shen
Mid-Space-Independent Symmetric Data Term for Pairwise DeformableImage Registration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 263
Iman Aganj, Eugenio Iglesias, Martin Reuter, Mert R. Sabuncu,and Bruce Fischl
A 2D-3D Registration Framework for Freehand TRUS-Guided ProstateBiopsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 272
Siavash Khallaghi, C. Antonio Sanchez, Saman Nouranian,Samira Sojoudi, Silvia Chang, Hamidreza Abdi, Lindsay Machan,Alison Harris, Peter Black, Martin Gleave, Larry Goldenberg,S. Sidney Fels, and Purang Abolmaesumi
Unsupervised Free-View Groupwise Segmentation for M3 CardiacImages Using Synchronized Spectral Network . . . . . . . . . . . . . . . . . . . . . . . . 280
Yunliang Cai, Ali Islam, Mousumi Bhaduri, Ian Chan, and Shuo Li
Uncertainty Quantification for LDDMM Using a Low-Rank HessianApproximation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 289
Xiao Yang and Marc Niethammer
A Stochastic Quasi-Newton Method for Non-Rigid ImageRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
Yuchuan Qiao, Zhuo Sun, Boudewijn P.F. Lelieveldt,and Marius Staring
Locally Orderless Registration for Diffusion Weighted Images . . . . . . . . . . 305Henrik G. Jensen, Francois Lauze, Mads Nielsen, and Sune Darkner
Predicting Activation Across Individuals with Resting-State FunctionalConnectivity Based Multi-Atlas Label Fusion . . . . . . . . . . . . . . . . . . . . . . . . 313
Georg Langs, Polina Golland, and Satrajit S. Ghosh
Contents – Part II XXXIII
Crossing-Lines Registration for Direct Electromagnetic Navigation . . . . . 321Brian J. Rasquinha, Andrew W.L. Dickinson, Gabriel Venne,David R. Pichora, and Randy E. Ellis
A Robust Outlier Elimination Approach for Multimodal Retina ImageRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
Ee Ping Ong, Jimmy Addison Lee, Jun Cheng, Guozhen Xu,Beng Hai Lee, Augustinus Laude, Stephen Teoh, Tock Han Lim,Damon W.K. Wong, and Jiang Liu
Estimating Large Lung Motion in COPD Patients by SymmetricRegularised Correspondence Fields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
Mattias P. Heinrich, Heinz Handels, and Ivor J.A. Simpson
Combining Transversal and Longitudinal Registrationin IVUS Studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346
G.D. Maso Talou, P.J. Blanco, I. Larrabide, C. Guedes Bezerra,P.A. Lemos, and R.A. Feijoo
Interpolation and Averaging of Multi-Compartment Model Images . . . . . 354Renaud Hedouin, Olivier Commowick, Aymeric Stamm,and Christian Barillot
Structured Decision Forests for Multi-modal Ultrasound ImageRegistration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 363
Ozan Oktay, Andreas Schuh, Martin Rajchl, Kevin Keraudren,Alberto Gomez, Mattias P. Heinrich, Graeme Penney,and Daniel Rueckert
A Hierarchical Bayesian Model for Multi-Site DiffeomorphicImage Atlases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 372
Michelle Hromatka, Miaomiao Zhang, Greg M. Fleishman,Boris Gutman, Neda Jahanshad, Paul Thompson,and P. Thomas Fletcher
Distance Networks for Morphological Profiling and Characterization ofDICCCOL Landmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 380
Yue Yuan, Hanbo Chen, Jianfeng Lu, Tuo Zhang, and Tianming Liu
Which Metrics Should Be Used in Non-linear RegistrationEvaluation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 388
Andre Santos Ribeiro, David J. Nutt, and John McGonigle
Modeling and Simulation for Diagnosis andInterventional Planning
Illustrative Visualization of Vascular Models for Static2D Representations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 399
Kai Lawonn, Maria Luz, Bernhard Preim, and Christian Hansen
XXXIV Contents – Part II
Perfusion Paths: Inference of Voxelwise Blood Flow Trajectories in CTPerfusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407
David Robben, Stefan Sunaert, Vincent Thijs, Guy Wilms,Frederik Maes, and Paul Suetens
Automatic Prostate Brachytherapy Preplanning Using Joint SparseAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 415
Saman Nouranian, Mahdi Ramezani, Ingrid Spadinger,William J. Morris, Septimiu E. Salcudean, and Purang Abolmaesumi
Bayesian Personalization of Brain Tumor Growth Model . . . . . . . . . . . . . . 424Matthieu Le, Herve Delingette, Jayashree Kalpathy-Cramer,Elizabeth R. Gerstner, Tracy Batchelor, Jan Unkelbach,and Nicholas Ayache
Learning Patient-Specific Lumped Models for Interactive CoronaryBlood Flow Simulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 433
Hannes Nickisch, Yechiel Lamash, Sven Prevrhal, Moti Freiman,Mani Vembar, Liran Goshen, and Holger Schmitt
Vito – A Generic Agent for Multi-physics Model Personalization:Application to Heart Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442
Dominik Neumann, Tommaso Mansi, Lucian Itu,Bogdan Georgescu, Elham Kayvanpour, Farbod Sedaghat-Hamedani,Jan Haas, Hugo Katus, Benjamin Meder, Stefan Steidl,Joachim Hornegger, and Dorin Comaniciu
Database-Based Estimation of Liver Deformation underPneumoperitoneum for Surgical Image-Guidance and Simulation . . . . . . . 450
S.F. Johnsen, S. Thompson, M.J. Clarkson, M. Modat, Y. Song,J. Totz, K. Gurusamy, B. Davidson, Z.A. Taylor, D.J. Hawkes,and S. Ourselin
Computational Sonography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459Christoph Hennersperger, Maximilian Baust, Diana Mateus,and Nassir Navab
Hierarchical Shape Distributions for Automatic Identification of 3DDiastolic Vortex Rings from 4D Flow MRI . . . . . . . . . . . . . . . . . . . . . . . . . . 467
Mohammed S.M. Elbaz, Boudewijn P.F. Lelieveldt,and Rob J. van der Geest
Robust CT Synthesis for Radiotherapy Planning: Application to theHead and Neck Region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 476
Ninon Burgos, M. Jorge Cardoso, Filipa Guerreiro, Catarina Veiga,Marc Modat, Jamie McClelland, Antje-Christin Knopf,Shonit Punwani, David Atkinson, Simon R. Arridge,Brian F. Hutton, and Sebastien Ourselin
Contents – Part II XXXV
Mean Aneurysm Flow Amplitude Ratio Comparison between DSA andCFD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 485
Fred van Nijnatten, Odile Bonnefous, Hernan G. Morales,Thijs Grunhagen, Roel Hermans, Olivier Brina,Vitor Mendes Pereira, and Daniel Ruijters
Application of L0-Norm Regularization to Epicardial PotentialReconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 493
Liansheng Wang, Xinyue Li, Yiping Chen, and Jing Qin
MRI-Based Lesion Profiling of Epileptogenic Cortical Malformations . . . 501Seok-Jun Hong, Boris C. Bernhardt, Dewi Schrader,Benoit Caldairou, Neda Bernasconi, and Andrea Bernasconi
Patient-specific 3D Ultrasound Simulation Based on ConvolutionalRay-tracing and Appearance Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . 510
Mehrdad Salehi, Seyed-Ahmad Ahmadi, Raphael Prevost,Nassir Navab, and Wolfgang Wein
Robust Transmural Electrophysiological Imaging: Integrating Sparseand Dynamic Physiological Models into ECG-Based Inference . . . . . . . . . 519
Jingjia Xu, John L. Sapp, Azar Rahimi Dehaghani, Fei Gao,Milan Horacek, and Linwei Wang
Estimating Biophysical Parameters from BOLD Signals throughEvolutionary-Based Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528
Pablo Mesejo, Sandrine Saillet, Olivier David, Christian Benar,Jan M. Warnking, and Florence Forbes
Radiopositive Tissue Displacement Compensation for SPECT-guidedSurgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536
Francisco Pinto, Bernhard Fuerst, Benjamin Frisch,and Nassir Navab
A Partial Domain Approach to Enable Aortic Flow Simulation WithoutTurbulent Modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 544
Taha S. Koltukluoglu, Christian Binter, Christine Tanner,Sven Hirsch, Sebastian Kozerke, Gabor Szekely,and Aymen Laadhari
Reconstruction, Image Formation, AdvancedAcquisition - Computational Imaging
Flexible Reconstruction and Correction of Unpredictable Motion fromStacks of 2D Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555
Bernhard Kainz, Amir Alansary, Christina Malamateniou,Kevin Keraudren, Mary Rutherford, Joseph V. Hajnal,and Daniel Rueckert
XXXVI Contents – Part II
Efficient Preconditioning in Joint Total Variation Regularized ParallelMRI Reconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 563
Zheng Xu, Yeqing Li, Leon Axel, and Junzhou Huang
Accessible Digital Ophthalmoscopy Based on Liquid-Lens Technology . . . 571Christos Bergeles, Pierre Berthet-Rayne, Philip McCormac,Luis C. Garcia-Peraza-Herrera, Kosy Onyenso, Fan Cao,Khushi Vyas, Melissa Berthelot, and Guang-Zhong Yang
Estimate, Compensate, Iterate: Joint Motion Estimation andCompensation in 4-D Cardiac C-arm Computed Tomography . . . . . . . . . . 579
Oliver Taubmann, Gunter Lauritsch, Andreas Maier,Rebecca Fahrig, and Joachim Hornegger
Robust Prediction of Clinical Deep Brain Stimulation Target Structuresvia the Estimation of Influential High-Field MR Atlases . . . . . . . . . . . . . . . 587
Jinyoung Kim, Yuval Duchin, Hyunsoo Kim, Jerrold Vitek,Noam Harel, and Guillermo Sapiro
RF Ultrasound Distribution-Based Confidence Maps . . . . . . . . . . . . . . . . . 595Tassilo Klein and William M. Wells III
Prospective Prediction of Thin-Cap Fibroatheromas from BaselineVirtual Histology Intravascular Ultrasound Data . . . . . . . . . . . . . . . . . . . . . 603
Ling Zhang, Andreas Wahle, Zhi Chen, John Lopez,Tomas Kovarnik, and Milan Sonka
Cooperative Robotic Gamma Imaging: Enhancing US-guided NeedleBiopsy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 611
Marco Esposito, Benjamin Busam, Christoph Hennersperger,Julia Rackerseder, An Lu, Nassir Navab, and Benjamin Frisch
Bayesian Tomographic Reconstruction Using Riemannian MCMC . . . . . . 619Stefano Pedemonte, Ciprian Catana, and Koen Van Leemput
A Landmark-Based Approach for Robust Estimation of Exposure IndexValues in Digital Radiography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 627
Paolo Irrera, Isabelle Bloch, and Maurice Delplanque
Accelerated Dynamic MRI Reconstruction with Total Variation andNuclear Norm Regularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 635
Jiawen Yao, Zheng Xu, Xiaolei Huang, and Junzhou Huang
Robust PET Motion Correction Using Non-local Spatio-temporalPriors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 643
S. Thiruvenkadam, K.S. Shriram, R. Manjeshwar,and S.D. Wollenweber
Contents – Part II XXXVII
Subject-specific Models for the Analysis of PathologicalFDG PET Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651
Ninon Burgos, M. Jorge Cardoso, Alex F. Mendelson,Jonathan M. Schott, David Atkinson, Simon R. Arridge,Brian F. Hutton, and Sebastien Ourselin
Hierarchical Reconstruction of 7T-like Images from 3T MRI UsingMulti-level CCA and Group Sparsity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 659
Khosro Bahrami, Feng Shi, Xiaopeng Zong, Hae Won Shin,Hongyu An, and Dinggang Shen
Robust Spectral Denoising for Water-Fat Separation in MagneticResonance Imaging . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 667
Felix Lugauer, Dominik Nickel, Jens Wetzl,Stephan A.R. Kannengiesser, Andreas Maier,and Joachim Hornegger
Scale Factor Point Spread Function Matching: Beyond Aliasing inImage Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 675
M. Jorge Cardoso, Marc Modat, Tom Vercauteren,and Sebastien Ourselin
Analytic Quantification of Bias and Variance of Coil Sensitivity ProfileEstimators for Improved Image Reconstruction in MRI . . . . . . . . . . . . . . . 684
Aymeric Stamm, Jolene Singh, Onur Afacan,and Simon K. Warfield
Mobile C-arm 3D Reconstruction in the Presence of UncertainGeometry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 692
Caleb Rottman, Lance McBride, Arvidas Cheryauka, Ross Whitaker,and Sarang Joshi
Fast Preconditioning for Accelerated Multi-contrast MRIReconstruction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 700
Ruoyu Li, Yeqing Li, Ruogu Fang, Shaoting Zhang, Hao Pan,and Junzhou Huang
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 709
Contents – Part III
Quantitative Image Analysis I: Segmentation andMeasurement
Deep Convolutional Encoder Networks for Multiple Sclerosis LesionSegmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Tom Brosch, Youngjin Yoo, Lisa Y.W. Tang, David K.B. Li,Anthony Traboulsee, and Roger Tam
Unsupervised Myocardial Segmentation for Cardiac MRI . . . . . . . . . . . . . . 12Anirban Mukhopadhyay, Ilkay Oksuz, Marco Bevilacqua,Rohan Dharmakumar, and Sotirios A. Tsaftaris
Multimodal Cortical Parcellation Based on Anatomical and FunctionalBrain Connectivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
Chendi Wang, Burak Yoldemir, and Rafeef Abugharbieh
Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta inFetal MRI Using One-Plane Scribbles and Online Learning . . . . . . . . . . . . 29
Guotai Wang, Maria A. Zuluaga, Rosalind Pratt, Michael Aertsen,Anna L. David, Jan Deprest, Tom Vercauteren,and Sebastien Ourselin
GPSSI: Gaussian Process for Sampling Segmentations of Images . . . . . . . 38Matthieu Le, Jan Unkelbach, Nicholas Ayache, and Herve Delingette
Multi-level Parcellation of the Cerebral Cortex Using Resting-StatefMRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
Salim Arslan and Daniel Rueckert
Interactive Multi-organ Segmentation Based on Multiple TemplateDeformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
Romane Gauriau, David Lesage, Melanie Chiaradia, Baptiste Morel,and Isabelle Bloch
Segmentation of Infant Hippocampus Using Common FeatureRepresentations Learned for Multimodal Longitudinal Data . . . . . . . . . . . 63
Yanrong Guo, Guorong Wu, Pew-Thian Yap, Valerie Jewells,Weili Lin, and Dinggang Shen
Measuring Cortical Neurite-Dispersion and Perfusion in Preterm-BornAdolescents Using Multi-modal MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
Andrew Melbourne, Zach Eaton-Rosen, David Owen, Jorge Cardoso,Joanne Beckmann, David Atkinson, Neil Marlow,and Sebastien Ourselin
XL Contents – Part III
Interactive Whole-Heart Segmentation in Congenital Heart Disease . . . . . 80Danielle F. Pace, Adrian V. Dalca, Tal Geva, Andrew J. Powell,Mehdi H. Moghari, and Polina Golland
Automatic 3D US Brain Ventricle Segmentation in Pre-Term NeonatesUsing Multi-phase Geodesic Level-Sets with Shape Prior . . . . . . . . . . . . . . 89
Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen,Martin Rajchl, Eranga Ukwatta, Sandrine de Ribaupierre,and Aaron Fenster
Multiple Surface Segmentation Using Truncated Convex Priors . . . . . . . . 97Abhay Shah, Junjie Bai, Zhihong Hu, Srinivas Sadda,and Xiaodong Wu
Statistical Power in Image Segmentation: Relating Sample Size toReference Standard Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
Eli Gibson, Henkjan J. Huisman, and Dean C. Barratt
Joint Learning of Image Regressor and Classifier for DeformableSegmentation of CT Pelvic Organs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
Yaozong Gao, Jun Lian, and Dinggang Shen
Corpus Callosum Segmentation in MS Studies Using Normal Atlasesand Optimal Hybridization of Extrinsic and Intrinsic Image Cues . . . . . . 123
Lisa Y.W. Tang, Ghassan Hamarneh, Anthony Traboulsee,David Li, and Roger Tam
Brain Tissue Segmentation Based on Diffusion MRI Using �0Sparse-Group Representation Classification . . . . . . . . . . . . . . . . . . . . . . . . . . 132
Pew-Thian Yap, Yong Zhang, and Dinggang Shen
A Latent Source Model for Patch-Based Image Segmentation . . . . . . . . . . 140George H. Chen, Devavrat Shah, and Polina Golland
Multi-organ Segmentation Using Shape Model Guided Local PhaseAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Chunliang Wang and Orjan Smedby
Filling Large Discontinuities in 3D Vascular Networks UsingSkeleton- and Intensity-Based Information . . . . . . . . . . . . . . . . . . . . . . . . . . 157
Russell Bates, Laurent Risser, Benjamin Irving,Bart�lomiej W. Papiez, Pavitra Kannan, Veerle Kersemans,and Julia A. Schnabel
A Continuous Flow-Maximisation Approach to Connectivity-DrivenCortical Parcellation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
Sarah Parisot, Martin Rajchl, Jonathan Passerat-Palmbach,and Daniel Rueckert
Contents – Part III XLI
A 3D Fractal-Based Approach Towards Understanding Changes in theInfarcted Heart Microvasculature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173
Polyxeni Gkontra, Magdalena M. Zak, Kerri-Ann Norton,Andres Santos, Aleksander S. Popel, and Alicia G. Arroyo
Segmenting the Uterus in Monocular Laparoscopic Images withoutManual Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
Toby Collins, Adrien Bartoli, Nicolas Bourdel, and Michel Canis
Progressive Label Fusion Framework for Multi-atlas Segmentation byDictionary Evolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
Yantao Song, Guorong Wu, Quansen Sun, Khosro Bahrami,Chunming Li, and Dinggang Shen
Multi-atlas Based Segmentation Editing with Interaction-GuidedConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198
Sang Hyun Park, Yaozong Gao, and Dinggang Shen
Quantitative Image Analysis II: Microscopy,Fluorescence and Histological Imagery
Improving Convenience and Reliability of 5-ALA-Induced FluorescentImaging for Brain Tumor Surgery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209
Hiroki Taniguchi, Noriko Kohira, Takashi Ohnishi,Hiroshi Kawahira, Mikael von und zu Fraunberg,Juha E. Jaaskelainen, Markku Hauta-Kasari, Yasuo Iwadate,and Hideaki Haneishi
Analysis of High-Throughput Microscopy Videos: Catching Up withCell Dynamics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218
A. Arbelle, N. Drayman, M. Bray, U. Alon, A. Carpenter,and T. Riklin Raviv
Neutrophils Identification by Deep Learning and Voronoi Diagram ofClusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226
Jiazhuo Wang, John D. MacKenzie, Rageshree Ramachandran,and Danny Z. Chen
U-Net: Convolutional Networks for Biomedical Image Segmentation . . . . 234Olaf Ronneberger, Philipp Fischer, and Thomas Brox
Co-restoring Multimodal Microscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . 242Mingzhong Li and Zhaozheng Yin
A 3D Primary Vessel Reconstruction Framework with Serial MicroscopyImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
Yanhui Liang, Fusheng Wang, Darren Treanor, Derek Magee,George Teodoro, Yangyang Zhu, and Jun Kong
XLII Contents – Part III
Adaptive Co-occurrence Differential Texton Space for HEp-2 CellsClassification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 260
Xiang Xu, Feng Lin, Carol Ng, and Khai Pang Leong
Learning to Segment: Training Hierarchical Segmentation under aTopological Loss . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 268
Jan Funke, Fred A. Hamprecht, and Chong Zhang
You Should Use Regression to Detect Cells . . . . . . . . . . . . . . . . . . . . . . . . . . 276Philipp Kainz, Martin Urschler, Samuel Schulter, Paul Wohlhart,and Vincent Lepetit
A Hybrid Approach for Segmentation and Tracking of MyxococcusXanthus Swarms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
Jianxu Chen, Shant Mahserejian, Mark Alber, and Danny Z. Chen
Fast Background Removal in 3D Fluorescence Microscopy Images UsingOne-Class Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 292
Lin Yang, Yizhe Zhang, Ian H. Guldner, Siyuan Zhang,and Danny Z. Chen
Motion Representation of Ciliated Cell Images with Contour-Alignmentfor Automated CBF Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
Fan Zhang, Yang Song, Siqi Liu, Paul Young, Daniela Traini,Lucy Morgan, Hui-Xin Ong, Lachlan Buddle, Sidong Liu,Dagan Feng, and Weidong Cai
Multimodal Dictionary Learning and Joint Sparse Representation forHEp-2 Cell Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 308
Ali Taalimi, Shahab Ensafi, Hairong Qi, Shijian Lu,Ashraf A. Kassim, and Chew Lim Tan
Cell Event Detection in Phase-Contrast Microscopy Sequences fromFew Annotations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 316
Melih Kandemir, Christian Wojek, and Fred A. Hamprecht
Robust Muscle Cell Quantification Using Structured Edge Detectionand Hierarchical Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324
Fujun Liu, Fuyong Xing, Zizhao Zhang, Mason Mcgough,and Lin Yang
Fast Cell Segmentation Using Scalable Sparse Manifold Learning andAffine Transform-Approximated Active Contour . . . . . . . . . . . . . . . . . . . . . 332
Fuyong Xing and Lin Yang
Restoring the Invisible Details in Differential Interference ContrastMicroscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340
Wenchao Jiang and Zhaozheng Yin
Contents – Part III XLIII
A Novel Cell Detection Method Using Deep Convolutional NeuralNetwork and Maximum-Weight Independent Set . . . . . . . . . . . . . . . . . . . . . 349
Fujun Liu and Lin Yang
Beyond Classification: Structured Regression for Robust Cell DetectionUsing Convolutional Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358
Yuanpu Xie, Fuyong Xing, Xiangfei Kong, Hai Su, and Lin Yang
Joint Kernel-Based Supervised Hashing for Scalable HistopathologicalImage Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366
Menglin Jiang, Shaoting Zhang, Junzhou Huang, Lin Yang,and Dimitris N. Metaxas
Deep Voting: A Robust Approach Toward Nucleus Localization inMicroscopy Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 374
Yuanpu Xie, Xiangfei Kong, Fuyong Xing, Fujun Liu, Hai Su,and Lin Yang
Robust Cell Detection and Segmentation in Histopathological ImagesUsing Sparse Reconstruction and Stacked Denoising Autoencoders . . . . . 383
Hai Su, Fuyong Xing, Xiangfei Kong, Yuanpu Xie, Shaoting Zhang,and Lin Yang
Automatic in Vivo Cell Detection in MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . 391Muhammad Jamal Afridi, Xiaoming Liu, Erik Shapiro,and Arun Ross
Quantitative Image Analysis III: Motion,Deformation, Development and Degeneration
Automatic Vessel Segmentation from Pulsatile Radial Distension . . . . . . . 403Alborz Amir-Khalili, Ghassan Hamarneh, and Rafeef Abugharbieh
A Sparse Bayesian Learning Algorithm for Longitudinal Image Data . . . . 411Mert R. Sabuncu
Descriptive and Intuitive Population-Based Cardiac Motion Analysisvia Sparsity Constrained Tensor Decomposition . . . . . . . . . . . . . . . . . . . . . . 419
K. McLeod, M. Sermesant, P. Beerbaum, and X. Pennec
Liver Motion Estimation via Locally Adaptive Over-SegmentationRegularization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 427
Bart�lomiej W. Papiez, Jamie Franklin, Mattias P. Heinrich,Fergus V. Gleeson, and Julia A. Schnabel
Motion-Corrected, Super-Resolution Reconstruction for High-Resolution 3D Cardiac Cine MRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 435
Freddy Odille, Aurelien Bustin, Bailiang Chen,Pierre-Andre Vuissoz, and Jacques Felblinger
XLIV Contents – Part III
Motion Estimation of Common Carotid Artery Wall Using a H∞ FilterBased Block Matching Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 443
Zhifan Gao, Huahua Xiong, Heye Zhang, Dan Wu, Minhua Lu,Wanqing Wu, Kelvin K.L. Wong, and Yuan-Ting Zhang
Gated-Tracking: Estimation of Respiratory Motion with Confidence . . . . 451Valeria De Luca, Gabor Szekely, and Christine Tanner
Solving Logistic Regression with Group Cardinality Constraints forTime Series Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 459
Yong Zhang and Kilian M. Pohl
Spatiotemporal Parsing of Motor Kinematics for Assessing StrokeRecovery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467
Borislav Antic, Uta Buchler, Anna-Sophia Wahl, Martin E. Schwab,and Bjorn Ommer
Longitudinal Analysis of Pre-term Neonatal Brain Ventricle inUltrasound Images Based on Convex Optimization . . . . . . . . . . . . . . . . . . . 476
Wu Qiu, Jing Yuan, Jessica Kishimoto, Yimin Chen,Martin Rajchl, Eranga Ukwatta, Sandrine de Ribaupierre,and Aaron Fenster
Multi-GPU Reconstruction of Dynamic Compressed Sensing MRI . . . . . . 484Tran Minh Quan, Sohyun Han, Hyungjoon Cho, and Won-Ki Jeong
Prospective Identification of CRT Super Responders Using a MotionAtlas and Random Projection Ensemble Learning . . . . . . . . . . . . . . . . . . . . 493
Devis Peressutti, Wenjia Bai, Thomas Jackson, Manav Sohal,Aldo Rinaldi, Daniel Rueckert, and Andrew King
Motion Compensated Abdominal Diffusion Weighted MRI bySimultaneous Image Registration and Model Estimation (SIR-ME) . . . . . 501
Sila Kurugol, Moti Freiman, Onur Afacan, Liran Domachevsky,Jeannette M. Perez-Rossello, Michael J. Callahan,and Simon K. Warfield
Fast Reconstruction of Accelerated Dynamic MRI Using ManifoldKernel Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 510
Kanwal K. Bhatia, Jose Caballero, Anthony N. Price, Ying Sun,Jo V. Hajnal, and Daniel Rueckert
Predictive Modeling of Anatomy with Genetic and Clinical Data . . . . . . . 519Adrian V. Dalca, Ramesh Sridharan, Mert R. Sabuncu,and Polina Golland
Contents – Part III XLV
Joint Diagnosis and Conversion Time Prediction of Progressive MildCognitive Impairment (pMCI) Using Low-Rank Subspace Clusteringand Matrix Completion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527
Kim-Han Thung, Pew-Thian Yap, Ehsan Adeli-M.,and Dinggang Shen
Learning with Heterogeneous Data for Longitudinal Studies . . . . . . . . . . . 535Letizia Squarcina, Cinzia Perlini, Marcella Bellani,Antonio Lasalvia, Mirella Ruggeri, Paolo Brambilla,and Umberto Castellani
Parcellation of Infant Surface Atlas Using Developmental Trajectoriesof Multidimensional Cortical Attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 543
Gang Li, Li Wang, John H. Gilmore, Weili Lin, and Dinggang Shen
Graph-Based Motion-Driven Segmentation of the CarotidAtherosclerotique Plaque in 2D Ultrasound Sequences . . . . . . . . . . . . . . . . 551
Aimilia Gastounioti, Aristeidis Sotiras, Konstantina S. Nikita,and Nikos Paragios
Cortical Surface-Based Construction of Individual Structural Networkwith Application to Early Brain Development Study . . . . . . . . . . . . . . . . . . 560
Yu Meng, Gang Li, Weili Lin, John H. Gilmore, and Dinggang Shen
Quantitative Image Analysis IV: Classification,Detection, Features, and Morphology
NEOCIVET: Extraction of Cortical Surface and Analysis of NeonatalGyrification Using a Modified CIVET Pipeline . . . . . . . . . . . . . . . . . . . . . . . 571
Hosung Kim, Claude Lepage, Alan C. Evans, A. James Barkovich,and Duan Xu
Learning-Based Shape Model Matching: Training Accurate Modelswith Minimal Manual Input . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 580
Claudia Lindner, Jessie Thomson, The arcOGEN Consortium,and Tim F. Cootes
Scale and Curvature Invariant Ridge Detector for Tortuous andFragmented Structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 588
Roberto Annunziata, Ahmad Kheirkhah, Pedram Hamrah,and Emanuele Trucco
Boosting Hand-Crafted Features for Curvilinear Structure Segmentationby Learning Context Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596
Roberto Annunziata, Ahmad Kheirkhah, Pedram Hamrah,and Emanuele Trucco
XLVI Contents – Part III
Kernel-Based Analysis of Functional Brain Connectivity on GrassmannManifold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 604
Luca Dodero, Fabio Sambataro, Vittorio Murino, and Diego Sona
A Steering Engine: Learning 3-D Anatomy Orientation UsingRegression Forests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 612
Fitsum A. Reda, Yiqiang Zhan, and Xiang Sean Zhou
Automated Localization of Fetal Organs in MRI Using Random Forestswith Steerable Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 620
Kevin Keraudren, Bernhard Kainz, Ozan Oktay,Vanessa Kyriakopoulou, Mary Rutherford, Joseph V. Hajnal,and Daniel Rueckert
A Statistical Model for Smooth Shapes in Kendall Shape Space . . . . . . . . 628Akshay V. Gaikwad, Saurabh J. Shigwan, and Suyash P. Awate
A Cross Saliency Approach to Asymmetry-Based Tumor Detection . . . . . 636Miri Erihov, Sharon Alpert, Pavel Kisilev, and Sharbell Hashoul
Grey Matter Sublayer Thickness Estimation in the Mouse Cerebellum . . 644Da Ma, Manuel J. Cardoso, Maria A. Zuluaga, Marc Modat,Nick Powell, Frances Wiseman, Victor Tybulewicz, Elizabeth Fisher,Mark F. Lythgoe, and Sebastien Ourselin
Unregistered Multiview Mammogram Analysis with Pre-trained DeepLearning Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652
Gustavo Carneiro, Jacinto Nascimento, and Andrew P. Bradley
Automatic Craniomaxillofacial Landmark Digitization viaSegmentation-Guided Partially-Joint Regression Forest Model . . . . . . . . . 661
Jun Zhang, Yaozong Gao, Li Wang, Zhen Tang, James J. Xia,and Dinggang Shen
Automatic Feature Learning for Glaucoma Detection Based on DeepLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669
Xiangyu Chen, Yanwu Xu, Shuicheng Yan,Damon Wing Kee Wong, Tien Yin Wong,and Jiang Liu
Fast Automatic Vertebrae Detection and Localization in PathologicalCT Scans - A Deep Learning Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 678
Amin Suzani, Alexander Seitel, Yuan Liu, Sidney Fels,Robert N. Rohling, and Purang Abolmaesumi
Guided Random Forests for Identification of Key Fetal Anatomy andImage Categorization in Ultrasound Scans . . . . . . . . . . . . . . . . . . . . . . . . . . 687
Mohammad Yaqub, Brenda Kelly, A.T. Papageorghiou,and J. Alison Noble
Contents – Part III XLVII
Dempster-Shafer Theory Based Feature Selection with SparseConstraint for Outcome Prediction in Cancer Therapy . . . . . . . . . . . . . . . . 695
Chunfeng Lian, Su Ruan, Thierry Denœux, Hua Li, and Pierre Vera
Disjunctive Normal Shape and Appearance Priors with Applications toImage Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 703
Fitsum Mesadi, Mujdat Cetin, and Tolga Tasdizen
Positive Delta Detection for Alpha Shape Segmentation of 3DUltrasound Images of Pathologic Kidneys . . . . . . . . . . . . . . . . . . . . . . . . . . . 711
Juan J. Cerrolaza, Christopher Meyer, James Jago, Craig Peters,and Marius George Linguraru
Non-local Atlas-Guided Multi-channel Forest Learning for HumanBrain Labeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719
Guangkai Ma, Yaozong Gao, Guorong Wu, Ligang Wu,and Dinggang Shen
Model Criticism for Regression on the Grassmannian . . . . . . . . . . . . . . . . . 727Yi Hong, Roland Kwitt, and Marc Niethammer
Structural Edge Detection for Cardiovascular Modeling . . . . . . . . . . . . . . . 735Jameson Merkow, Zhuowen Tu, David Kriegman,and Alison Marsden
Sample Size Estimation for Outlier Detection . . . . . . . . . . . . . . . . . . . . . . . . 743Timothy Gebhard, Inga Koerte, and Sylvain Bouix
Multi-scale Heat Kernel Based Volumetric Morphology Signature . . . . . . 751Gang Wang and Yalin Wang
Structural Brain Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 760Muhammad Razib, Zhong-Lin Lu, and Wei Zeng
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 769