Center for Computational Imaging and Personalized Diagnostics (CCIPD)

2012 Annual Report

Director: Dr. Anant MadabhushiAssociate Professor,

Department of Biomedical Engineering

LCIB-CCIPD Transition

LCIB Website

CCIPD LG meeting schedule webpage

We have moved! LCIB is now the Center of Computational Imaging and Personalized Diagnostics (CCIPD) within the Department of Biomedical Engineering at Case Western Reserve University.

Dr. Madabhushi and 12 members relocated to start CCIPD at CWRU.

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CCIPD in 2012

Faculty Offices Center Space Center Entrance

525 Wickenden BuildingDept. of Biomedical EngineeringCase Western Reserve University2071 Martin Luther King Drive

Cleveland, Ohio 44106-7207

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Center Members

Farewell dinner in NJ, August 2012

Group dinner in Cleveland, Sept. 2012

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CCIPD Members

Research Faculty Satish Viswanath, PhD Pallavi Tiwari, PhD

Research Associates Mirabela Rusu, PhD Tao Wan, PhD Haibo Wang, PhD

Undergraduate Students Eileen Huang* Sudha Karthigeyan* Aparna Kannan* Srivathsan Prabhu* Gabriel Ewing

Julian Modesto * Michael Yim*

Graduate Students Andrew Janowczyk (IIT, Bombay) Rachel Sparks (Rutgers, NJ) Ajay Basavanhally (Rutgers, NJ) George Lee (Rutgers, NJ) Rob Toth (Rutgers, NJ) Shoshana Ginsburg Sahir Ali Asha Singanamalli

* At Rutgers University

Center Director: Dr. Anant Madabhushi

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Center Members

Center DirectorDr. Anant Madabhushi, PhD

Research Faculty

Pallavi Tiwari, PhD Satish Viswanath, PhDResearch Associates

Mirabela Rusu, PhD Tao Wan, PhD Haibo Wang, PhD

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Center MembersGraduate Students

Ajay Basavanhally Andrew Janowczyk George Lee Shoshana GinsburgSahir Ali

Rachel Sparks Rob Toth Asha Singanamalli

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Recent Alumni

Jun Xu, PhDProfessor at Nanjing University

Research Faculty

Gaoyu Xiao, PhD

PhD Students

PostDocs

James Monaco, PhDResearch Scientist atVuCOMP

Pallavi Tiwari, PhDGraduated from LCIB in May, 2012Research Faculty at Case Western Reserve University

Satish Viswanath, PhDGraduated from LCIB in May, 2012Research Faculty at Case Western Reserve University

Shannon Agner4th year medical student at UMDNJ-RWJMS

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Recent AlumniPhD Students

Master Students

Jon Chappelow, PhDResearch Scientist atAccuray Inc.

Scott Doyle, PhDResearch Scientist at Ibris Inc.

Akshay ShridarAssistant ProjectManager at IntegraLife Sciences

Najeeb ChowdhuryHealthcare MarketResearch Analystat AlphaDetail Inc.

Rhonda Breen-SimoneAdministrative Assistant at Dept. of Nutritional Sciences, Rutgers University

Assistant

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Recent Alumni

Joe GaleroGraduate Student at Johns Hopkins

University

Elaine YuGraduate Student at UC Berkeley

Pratik PatelIntern at BioLite

Abhishek GolugulaEmployee at Accenture

Undergraduate Students

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Acknowledging just some of our outstanding clinical collaborators

John E. Tomaszewski with Bill ClintonDan Sperling (left), Michael D. Feldman (second from right), B. Nicolas Bloch (right)

CCIPD also acknowledges the larger number of excellent clinical collaborators across CINJ, Rutgers, Case Western, MD Anderson, UT Southwestern Dallas, UCSF, UPENN, SUNY Buffalo, NYU we have been privileged to work with.

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Student Accomplishments

Pallavi Tiwari and Satish Viswanath at the 2012 Graduate Reception having obtained

their PhDs, May 2012.

Group photo with Pallavi Tiwari & Satish Viswanath immediately after their

successful PhD thesis defense, April 2012

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Conference Participation 2012

RSNA: Satish Viswanath delivering an oral talk, Nov. 2012.

SPIE 2012 Best Poster (Cum Laude) in CAD: Rachel Sparks

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Conference Participation 2012

VLPR: Sahir AliVLPR: Rob Toth (left)

Sahir Ali (right)

VLPR: Sahir Ali, Rob Toth, Anant Madabhushi, Jun Xu (alum) with Dr. Xu’s graduate students (standing)

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CCIPD Workshop 2012

Dr Madabhushi attended and presented at the Radio-omics Workshop organized by Dr. Bob Gillies at the Moffit Cancer Center in Tampa, FL. Also attendance were folks from the Cancer Imaging Program at NCI (Drs Larry Clarke and Nordstrom) and members of the Quantitative Imaging Network (QIN).

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CCIPD Workshop 2012

The HIMA (Histopathology Image Analysis) workshop – 4th year running of HIMA at MICCAI 2012

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CCIPD Workshop 2012

The prostate imaging challenge workshop- first prostate segmentation challenge at MICCAI attended by 13 participating teams.

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Summary of Accomplishments 2012 Lab Members: 20

Faculty: 3 Research Associates: 3 Graduate Students: 8 Undergraduate Students: 7

Theses (3): 2 PhD + 1 MS Books: 1 Journal Papers: 19 Peer-Reviewed Conference Papers: 19 Peer reviewed Abstracts: 7

Issued Patents: 3Grants: 4Ongoing Projects: 31

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Peer Reviewed Publications for 2012

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Theses Journal Papers Conference Papers Abstracts

Summary

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Peer Reviewed PublicationsJournal Papers

Shridar, A, Doyle, S, Madabhushi, A, “Content-Based Image Retrieval of Digitized Histopathology in Boosted Spectrally Embedded Spaces”, IEEE Transactions on Information Technology in Biomedicine, Minor Changes.

Toth, R, Gentile, J, Sperling, D, Madabhushi, A, “Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models with Multiple Coupled Levelsets”, Computer Vision and Image Understanding, Minor changes.

Janowczyk, A, Chandran, S, Madabhushi, A, “Quantifying local heterogeneity via morphologic scale: Distinguishing tumoral regions from stromal regions”, Journal of Pathology Informatics, Accepted.

Tiwari, P, Kurhanewicz, J, Madabhushi, A, “Multi-Kernel Graph Embedding for Detection, Gleason Grading of Prostate Cancer via MRI/MRS”, Medical Image Analysis, Accepted.

Doyle, S, Tomaszewski, J, Feldman, M, Shih, N, Madabhushi, A, “Cascaded Multi-Class Pairwise Approach to Automated Classification of Normal, Cancerous, and Confounder Classes of Prostate Tissue”, BMC Bioinformatics, 2012 Oct 30;13(1):282. [Epub ahead of print] PMID: 23110677.

Agner, S, Xu, J, Madabhushi, A, “Spectral Embedding based Active Contour (SEAC) for Lesion Segmentation on Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging”, Medical Physics, Accepted.

Gurcan, M, Madabhushi, A, Medical Imaging 2013: Digital Pathology (Proceedings Volume), Proceedings of SPIE Volume: 8676, ISBN: 9780819494504, 2013.

Books

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Peer Reviewed PublicationsJournal Papers (Contd.)

Monaco, J, Madabhushi, A, “Class-Specific Weighting for Markov Random Field Estimation: Application to Medical Image Segmentation”, Medical Image Analysis, [Epub ahead of print](PMID: 22986078).

Ali, S, Madabhushi, A, “An Integrated Region, Boundary, Shape based Active Contour for multiple object overlap resolution in histological imagery”, IEEE Transactions on Medical Imaging, vol. 31[7], pp. 1448-60, 2012 (PMID: 22498689).

Toth, R, Madabhushi, A, “Multi-Feature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation”, IEEE Transactions on Medical Imaging, vol. 31[8], pp. 1638-50, 2012, (PMID: 22665505).

Viswanath, S, Madabhushi, A, “Consensus Embedding: Theory, Algorithms and Application to Segmentation and Classification of Biomedical Data”, BMC Bioinformatics, 13(1): 26, 2012 (PMID: 22316103) (Highly accessed).

Hipp, J, Monaco, J, Kunju, P Cheng, P, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Hewitt, S, Feldman, M, Tomaszewski, J, Shih, N, Toner, M, Tompkins, R, Flotte, T, Lucas, D, Gilbertson, Kunju, LP, J, Balis, U, Madabhushi, A, “Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification”, Analytical Cellular Pathology, (PMID: 22425661).

Chowdhury, N, Toth, R, Chappelow, J, Kim, S, Motwani, S, Punekar, S, Lin, H, Both, S, Vapiwala, N, Hahn, S, Madabhushi, A, “Concurrent segmentation of the prostate on MRI and CT via linked statistical shape models for radiotherapy planning”, Medical Physics, vol. 39(4), pp. 2214-28, 2012 (PMID: 22482643) (Editors Pick).

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Peer Reviewed PublicationsJournal Papers (Contd.) Viswanath, S, Bloch, B, Chappelow, J, Toth, R, Rofsky, N, Genega, E, Lenkinski, R, Madabhushi, A,

“Central Gland and Peripheral Zone Prostate Tumors have Significantly Different Quantitative Imaging Signatures on 3 Tesla Endorectal, In Vivo T2-Weighted Magnetic Resonance Imagery”, Journal of Magnetic Resonance Imaging, 2012 [Epub ahead of print] (PMID: 22337003).

Ghaznavi, D, Evans, A, Madabhushi, A, Feldman, M, “Digital Imaging in Pathology: Whole-Slide Imaging and Beyond”, Annual Review of Pathology: Mechanisms of Disease, vol. 19[3], pp. 331-359, 2012 (PMID: 23157334).

Ali, S, Madabhushi, A, "GPU Implementation of an Integrated Shape Based Active Contour: Application to Digital Pathology", Journal of Pathology Informatics, 2:13, 2012, (PMID: 22811957).

Basavanhally, A, Ganesan, S, Shih, N, Feldman, M, Tomaszewski, J, Madabhushi, A, “Multi-Field-of-View Strategy for Image-Based Outcome Prediction of ER+ Breast Cancer Histopathology Using Spatio-Architectural and Vascular Features”, Journal of Pathology Informatics, 2:1, 2012, (PMID: 22665505).

Bulman, J, Toth, R, Patel, AD, Bloch, BN, McMahon, CJ, Ngo, L, Madabhushi, A, Rofsky, N, Automated Computer-Derived Prostate Volumes from MRI Data: Comparison to Radiologist-Derived MRI Volumes and Pathology Specimen Volumes, Radiology, vol. 262[1], pp. 144-151, 2012 (PMID: 22190657).

Doyle, S, Tomaszewski, J, Feldman, M, Madabhushi, A, A Boosted Bayesian Multi-Resolution Classifier for Prostate Cancer Detection from Digitized Needle Biopsies, IEEE Transactions on Biomedical Engineering, vol. 59(5), pp. 1205-18, 2012 (PMID: 20570758).

Janowczyk, A, Chandran, S, Singh, R, Sasaroli, D, Coukos, G, Feldman, M, Madabhushi, A, “High-Throughput Biomarker Segmentation on Ovarian Cancer Tissue Microarrays via Hierarchical Normalized Cuts”, IEEE Trans on Biomedical Engineering, vol. 59(5), pp. 1240-1252, 2012 (PMID: 22180503).

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Peer Reviewed PublicationsPeer-reviewed Conference Papers

Janowczyk, A, Chandran, A, Madabhushi, A, “Quantifying local heterogeneity via morphologic scale: Distinguishing tumor from stroma”, Workshop on Histopathologic Image Analysis, In Conjunction with Medical Image Computing and Computer Assisted Interventions (MICCAI), (http://www2.warwick.ac.uk/fac/sci/dcs/events/hima2012/hima2012_programdraft.pdf), 2012.

Toth, R, Madabhushi, A, “Deformable Landmark-Free Active Appearance Models: Application to Segmentation of Multi-institutional Prostate MRI Data”, (http://promise12.grand-challenge.org/Results/Overview), MICCAI Grand Challenge: Prostate MR Image Segmentation (PROMISE) 2012.

Cruz Roa, A, Galero, J, Osorio, FA, Madabhushi, A, Romero, E, “A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slides”, In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI), vol. 1, pp. 157-164, 2012.

Monaco, J, Madabhushi, A, “Image Segmentation with Implicit Color Standardization Using Spatially Constrained Expectation Maximization: Detection of Nuclei”, In Proc of Medical Image Computing and Computer Assisted Interventions (MICCAI), vol. 1, pp. 365-372, 2012.

Monaco, J, Raess, P, Chawla, R, Bagg, A, Weiss, M, Choi, J, Madabhushi, A, “Image Segmentation with Implicit Color Standardization Using Cascaded EM: Detection of Myelodysplastic Syndromes”, IEEE International Symposium on Biomedical Imaging, pp. 740-743, 2012.

Rusu, M, Bloch BN, Jaffe C, Rofsky N, Genega E, Lenkinski R, Madabhushi A, “Statistical 3D Prostate Imaging Atlas Construction via Anatomically Constrained Registration”, SPIE Medical Imaging, 2013.

Rusu M, Wang H, Golden T, Gow A, Madabhushi A, “Multi-Scale, Multi-Modal Fusion of Histological and MRI Lung Volumes for Characterization of Airways”, SPIE Medical Imaging, 2013.

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Peer Reviewed PublicationsPeer-reviewed Conference Papers (Contd.)

Wan T, Bloch BN, Madabhushi A, “A Novel Point-based Nonrigid Image Registration Scheme Based on Learning Optimal Landmark Configurations”, SPIE Medical Imaging, 2013.

Tiwari, P, Danish, S, Wong, S, Madabhushi A, “Quantitative Evaluation of Multi-parametric MR Imaging Marker Changes Post-laser Interstitial Ablation Therapy (LITT) for Epilepsy”, SPIE Medical Imaging, 2013.

Singanamalli, A, Sparks, R, Rusu, M, Shih, N, Ziober, A, Tomaszewski, JE, Rosen, M, Feldman, M, Madabhushi, A “Correlating in vivo Imaging and ex vivo Vascular Markers to Identify Aggressive Prostate Cancer: Preliminary Results”, SPIE Medical Imaging, 2013.

Prabu SB, Toth R, Madabhushi A, “A Statistical Deformation Model (SDM) based Regularizer for Non-rigid Image Registration: Application to registration of multimodal prostate MRI and histology”, SPIE Medical Imaging, 2013.

Sparks R, Feleppa E, Barratt D, Bloch BN, Madabhushi A, “Fully Automated Prostate MRI and Transrectal Ultrasound(TRUS) Fusion via a Probabilistic Registration Metric”, SPIE Medical Imaging, 2013.

Ginsburg SB, Bloch BN, Rofsky NM, Genega E, Lenkinski RN, Madabhushi A, “Iterative Multiple Reference Tissue Method for Estimating Pharmacokinetic Parameters on Prostate DCE MRI”, SPIE Medical Imaging, 2013.

Ali S, Veltri R, Epstein J, Christudass C, Madabhushi A, “Cell Cluster Graph for Prediction of Biochemical Recurrence in Prostate Cancer Patients from Tissue Microarrays”, SPIE Medical Imaging, 2013.

Wang H, Rusu M, Golden T, Gow A, Madabhushi A, “Mouse Lung Volume Reconstruction from Efcient Groupwise Registration of Individual Histological Slices with Natural Gradient”, SPIE Medical Imaging, 2013.

Basavanhally A, Madabhushi A, “EM-based Segmentation-Driven Color Standardization of Digitized Histopathology”, SPIE Medical Imaging, 2013.

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Peer Reviewed Publications

2/11/2015 25

Peer-reviewed Conference Papers (Contd.)

Viswanath S, Sperling D, Lepor H, Futterer J, Madabhushi A, “Quantitative evaluation of treatment related changes on multi-parametric MRI after laser interstitial thermal therapy of prostate cancer, SPIE Medical Imaging, 2013.

Incorporating the whole-mount prostate histology reconstruction program Histostitcher© into the extensible imaging platform (XIP) framework, Toth, R, Chappelow, J, Kutter, O, Vetter, C, Russ, C, Feldman, M, Tomaszewski, J, Shih, N, Madabhushi, A, SPIE Medical Imaging, 2012, Accepted.

Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM), Sparks, R, Madabhushi, A, SPIE Medical Imaging, 2012, Accepted (Cum Laude, Best Poster in CAD Conference at SPIE Medical Imaging 2012).

Peer Reviewed Abstracts

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Lewis, J, Ali, S, Thorstad, W, Madabhushi, A, “A Quantitative Histomorphometric Classifier Identifies Aggressive Versus Indolent p16 Positive Oropharyngeal Squamous Cell Carcinoma”, United States and Canadian Academy of Pathology's 102nd Annual Meeting, 2013, Accepted.

Viswanath, S, Madabhushi, A, “Evaluating the need for intensity artifact correction in multi-parametric MRI for computerized detection of prostate cancer in vivo”, Society for Imaging Informatics in Medicine, 2013.

Viswanath, S, Bloch, N, Lenkinski, R, Rofsky, N, Madabhushi, A, “Computer-aided Detection of Peripheral Zone and Central Gland Prostate Tumors on T2-weighted MRI”, Proceedings of the Radiologic Society of North America 2012, Accepted.

Ali, S, Lewis, J, Madabhushi, A, “Use of quantitative histomorphometrics to classify disease progression in HPV-positive squamous cell carcinoma “, J Clin Oncol 30: 2012 (suppl 30; abstr 73).

Veltri, R, Christudass, C, Epstein, J, Ali, S, Yoon, H-J, Li, C-C, Madabhushi, A, Computer-assisted Gleason grading of Prostate Cancer: Two novel approaches using nuclear shape and texture feature to classify pathologic Gleason grade patterns 3 and 4, American Association for Cancer Research, 2012.

Reiss, P, Monaco, J, Bagg, A, Weiss, M, Madabhushi, A, Choi, J, “Alpha-Hemoglobin Stabilizing Protein Specifically Identifies Nucleated Erythroid Precursors and Enables Identification of Architectural Distortion in Myelodysplastic Syndromes by Computerized Image Analysis”, United States and Canadian Academy of Pathology's 101st Annual Meeting, March 17-23, 2012, Vancouver, BC, Canada.

Hipp, J, Monaco, J, Kunju, P Cheng, P, Yagi, Y, Rodriguez-Canales, J, Emmert-Buck, M, Hewitt, S, Feldman, M, Tomaszewski, J, Shih, N, Toner, M, Tompkins, R, Flotte, T, Lucas, D, Gilbertson, Kunju, LP, J, Balis, U, Madabhushi, A, “Integration of architectural and cytologic driven image algorithms for prostate adenocarcinoma identification”, Modern Pathology, vol. 25[2], pp. 213A-214A, 2012.

PatentsIssued Patents

“Systems and Methods for Classification of Biological Datasets", Anant Madabhushi, Michael D. Feldman, Jianbo Shi, Mark Rosen, John Tomaszewski, United States Serial Number (USSN): 8,204,315.

“Malignancy Diagnosis Using Content-Based Image Retrieval of Tissue Histopathology”, Anant Madabhushi, Michael D. Feldman, John Tomaszewski, Scott Doyle, United States Serial Number (USSN): 8,280,132.

“Computer Assisted Diagnosis (CAD) of cancer using Multi-Functional Multi-Modal in vivo Magnetic Resonance Spectroscopy (MRS) and Imaging (MRI)", Anant Madabhushi, Satish Viswanath, Pallavi Tiwari, Robert Toth, Mark Rosen, John Tomaszewski, Michael D. Feldman, United States Serial Number (USSN): 8,295,575.

Patents Pending

“Method and Apparatus for Shape based deformable segmentation of multiple overlapping objects”, Anant Madabhushi, Sahirzeeshan Ali, PCT/US12/20816, Jan. 2012.

“Local Morphologic Scale with applications to Multi-protocol Registration and Tissue classification”, Anant Madabhushi, Andrew Janowczyk, Sharat Chandran, PCT/US12/21133, Jan. 2012.

“Enhanced Multi-Protocol Analysis via Intelligent Supervised Embedding (EMPrAvISE) for Multimodal Data Fusion”, Anant Madabhushi, Satish Viswanath, PCT/US12/21373.

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PatentsProvisional Patents Applications

Method and Apparatus for an Integrated, Multivariate Histologic Image-based risk predictor for outcome of ER+ breast cancer”, Anant Madabhushi, Ajay Basavanhally, RU 2011-160.

“Method and Apparatus for Registering Image Data Between Different Types of Image Data”, Anant Madabhushi, Rachel Sparks, P05561US0.

“Color Standardization of Digital Histological Images”, Anant Madabhushi, Ajay Basavanhally, US Provisional Application No: 61/715,645.

Invention Disclosures

“A Texture Based Finite Element Model Registration Scheme For Registering Pre-Treatment Intensity Modulated Radiation Therapy MR Imagery To Post-Treatment MR Imagery”, Anant Madabhushi, Robert Toth, Case 2013-2375.

“Discriminatively weighted multi-scale local binary patterns for object detection”, Anant Madabhushi, Haibo Wang, Case 2013-2391.

“Co-occurring Gland Tensors in Localized Cluster Graphs for Quantitative Histomorphometry”, Anant Madabhushi, George Lee, Sahir Ali, Rachel Sparks, Case 2013-2369.

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Awards and Accomplishments in 2012Professional, Editorial, Activities, Institutional Service – Anant Madabhushi

Chair, Poster Session Analysis of Microscopic and Optical Images II, MICCAI 2012, October 4th, 2012, Nice, France Member of the MICCAI 2012 Young Scientist Award Committee. Area Chair, Eight Indian Conference on Vision, Graphics, and Image Processing (ICVGIP) 2012. Chair, Prostate Segmentation Challenge, MICCAI 2012. Chair, Workshop on Histopathology Image Analysis, MICCAI 2012. Advisory Committee, Workshop: Bio-computing, Genomics and Epigenomics, DIMAC, Rutgers University,

September 2012. Panelist and Invited Speaker, “Careers in Biomedical Engineering”, Engineering Governing Council, Rutgers

University, March, 2012. Program Committee Member, Imaging Track of the 2nd annual IEEE International Conference on Healthcare

Informatics, Imaging and Systems Biology(HISB), 2012. Associate Editor, IEEE Transactions in Biomedical Engineering 2009-present. Associate Editor, IEEE Transactions in Biomedical Engineering (Letters) 2008-present. Program Committee Member, Asian Conference on Computer Vision (ACCV 2012), 2012. Program Committee Member, ASE/IEEE International Conference on BioMedical Computing, 2012. Program Committee Member, MICCAI 2012. Chair, Conference 8315: Computer Aided Diagnosis, Session on Abdomen Imaging, SPIE Medical Imaging, Feb 2012. Chair, Conference 8315: Computer Aided Diagnosis, Session on Digital Pathology I, SPIE Medical Imaging, Feb 2012. Chair, Conference 8315: Computer Aided Diagnosis, Session on Digital Pathology II, SPIE Medical Imaging, Feb

2012.

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Awards and Accomplishments

Awards RSNA Abstract “Computer-aided Detection of Peripheral Zone and Central Gland Prostate Tumors on T2-

weighted MRI”, Featured on AuntMinnie.com in the Advanced Visualization preview, 2012. Runner Up for Investors Choice Award, New Jersey Entrepreneurial Network (NJEN), 2012.

Media Recognition “ECE Student awarded travel grant to attend VLPR 2012”, Featured on front page of Rutgers Department of

Electrical Engineering Homepage, July 23, 2012, http://www.ece.rutgers.edu/node/947 “Translational Research: Great Promise and Major Challenges”, LifeSci Trends, March 2012, vol. 11[1], pp.

17. “Building a better predictor”, Page 6, CINJ Oncolyte, Winter 2012 Issue, February 2012. “Startups Present Posters at Packed NJEN Event”, New Jersey Tech Weekly, February 7th, 2012.

Professional, Editorial, Activities, Institutional Service – Anant Madabhushi (Contd.) Program Committee Member, Workshop on Machine Learning in Medical Imaging, MLMI'12. Program Committee Member, International Conference on Image and Signal Processing, ICISP'12. Co-Chair, Workshop on Non-invasive imaging/imaging phenome, American Society for Investigative

Pathology, San Diego, 2012.

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New Grants-2012

Madabhushi, Anant (PI) Date: 07/01/12-06/31/13Proof of Concept Award, RutgersComputerized Decision Support for Prostate Cancer Detection and Treatment via Multi-parametric MRIRole: PI

Madabhushi, Anant (PI) Date: 9/12/12-8/31/14NIH R21CA167811-01Decision support with MRI for targeting, evaluating laser ablation for prostate cancersRole: PI

Buckler, Andrew (PI)Date: 01/01/13 - 06/01/13NSF SBIR Phase 1Computer assisted prognosis of debilitating diseaseRole: Co-PI (Madabhushi)

Madabhushi, Anant (PI) Date: 01/01/13-12/31/13QED Grant, University City Science CenterProstaCAD™: Computer based detection of Prostate Cancer from MRIRole: PI

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Student, Post-doctoral Awards and Fellowships Andrew Janowcyz, Travel award ($500) to attend HIMA Workshop in conjunction with MICCAI

2012. Sahirzeeshan Ali, Travel award ($2000) to attend USA-Sino Summer School in Vision,

Learning, Pattern Recognition: VLPR 2012 (Theme: Computer Vision in Biomedical Image Applications: From Micro to Macro), 2012

Robert Toth, Travel award ($2000) to attend USA-Sino Summer School in Vision,Learning, Pattern Recognition: VLPR 2012 (Theme: Computer Vision in Biomedical Image Applications: From Micro to Macro), 2012

Angel Alfonso Cruz Roa, MICCAI Travel Award ($600), 2012. Srivatsan Babu, Aresty 2012-2013 RA Award, “Characterizing Elastic Properties of the Prostate”,

May 2012. Eileen Hwuang, First Place (Overall) for Best Student Research Presentation ($250), SOE Research

Symposium, Rutgers University, 2012 Joseph Galero, Fulbright Fellowship, 2012 Najeeb Chowdhury, “Concurrent segmentation of the prostate on MRI and CT via linked statistical

shape models for radiotherapy planning”, Highlighted in Medical Physics Editors Pick Column. Satish Viswanath, “Consensus Embedding: Theory, Algorithms and Applications to Biomedical

Segmentation and Classification”, BMC Bioinformatics Highly accessed paper. Elaine Yu, NSF Graduate Research Fellowship, 2012 Joseph Galero, Honorable Mention, NSF Graduate Research Fellowship, 2012 Rachel Sparks, Cum laude for Best Poster Presentation at the Computer Aided Diagnosis (CAD)

Conference, SPIE Medical Imaging, 2012

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Active Research (32 projects)

Methods

Multimodal Data Integration and

Evaluating Therapy

Computer Aided Diagnosis and Prognosis

Image Segmentation

Machine Learning

Image Registration

Radiology

Histopathology

Bioinformatics

Prostate Cancer

Breast Cancer

Brain Glioma

Image Reconstruction

Epilepsy

Medulloblastoma

Oropharyangeal

Carotid Plaque Myelodysplasia

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IMAGE SEGMENTATION

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Spectral Embedding based Active Contour (SEAC) for Lesion Segmentation on Breast MRI

(a) Original grayscale post-contrastimage and image representationsderived from (b) FCM and (c) SE.Note that the colormaps displayedfor both methods only reflect thevoxel similarities and determinedby the 2 schemes, voxels withsimilar time-intensity curves beingassigned similar colors. The secondrow of images shows the groundtruth segmentation (e) in red and thehybrid AC segmentation driven by(f) the FCM+AC segmentationoverlaid (yellow line) on groundtruth, (g) PCA+AC segmentation,and (h) SEAC segmentationoverlaid on ground truth.

Quantitative results obtainedfrom the SEAC segmetnationcompared to the FCM+AC, andPCA+AC methods.

Agner S., Madabhushi, A., Xu J., “Spectral Embedding based Active Contour (SEAC) for Lesion Segmentation on Breast Dynamic Contrast Enhanced Magnetic Resonance Imaging,” Proc. SPIE 7963, Medical Imaging 2011: Computer Aided Diagnosis, 796305.

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Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models with Multiple Coupled Levelsets

• Use of Active Appearance Models (AAM’s) to couple multiple levelsets using PCA

• Simultaneous segmentation of prostate, central gland (CG), peripheral zone (PZ).

Toth, R., Ribault, J., Gentile, J., Sperling, D., Madabhushi, A., “Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models with Multiple Coupled Levelsets,” CVIU, 2013. Pending Minor Revisions.

Figure 1. AAM coupling multiple shapes

Figure 2. Segmentation result for Study #1.

Figure 3. Segmentation result for Study #2.

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J Monaco, J Hipp, David Lucas, U Balis, S Smith, A Madabhushi, “Image Segmentation with Implicit Color Standardization Using Spatially Constrained Expectation Maximization: Detection of Nuclei,” MICCAI 2012.

Most EM-based algorithms ignore spatial constraints. Thus, we presented spatially-constrained EM (SCEM), a novel approach for incorporating Markov priors into the EM framework. We validated SCEM by integrating it into our computerized system to segment nuclei in H&E stained histopathology.

Image Segmentation with Implicit Color Standardization Using Spatially Constrained

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J Monaco, P Raess, R Chawla, A Bagg, M Weiss, J Choi, A Madabhushi, “Image Segmentation with Implicit Color Standardization Using Cascaded EM: Detection of Myelodysplastic Syndromes,” ISBI 2012.

Color nonstandardness − the propensity for similar objects to exhibit different color properties across images − poses a significant problem in digital histopathology. We developed a unique instantiation of the expectation maximization (called cascaded EM) to help create a novel color segmentation algorithm which is robust to nonstandardness. We validated our algorithm by detecting myelodysplasticsyndrome on bone marrow specimens.

Image Segmentation with Implicit Color Standardization Using Cascaded EM Detection of Myelodysplastic

Syndromes

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A Region-boundary and Shape based Active Contour

Ali, Sahirzeeshan and Madabhushi, Anant “An Integrated Region, Boundary, Shape based Active Contour for multiple object overlap resolution in histological imagery”, IEEE Trans on Medical Imaging, 2012.

Hybrid Active Contour scheme that can simultaneously segment overlapping and non-overlapping objects.

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EM-Based Segmentation-Driven Color Standardization of Digitized Histopathology

0 50 100 150 200 250

0 50 100 150 200 250

Goal: Improve color constancy across histology images by realigning color distributions to match a pre-defined template.

(a) A new histopathology image isstandardized to (b) a template imageusing the (c) Expectation Maximizationalgorithm to decompose the image intotissue classes. (d) Color histograms arealigned separately for each class andsubsequently recombined to form astandardized image. Green outlinesillustrate how standardization yieldsmore consistent segmentation of nuclei.In addition, histograms demonstratingalignment of multiple images (a) beforeand (d) after standardization.

(a)

(b)

(c) (d)

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Multi-Feature, Landmark-Free Active Appearance Models (MFLAAM)

• Levelset function used to create statistical shape model in an AAM framework.

• Generalized AAM’s to incorporate image derived attributes such as gradient or texture information – only considered intensities previously

• “Registration”-like template matching scheme to locate prostate

Error

Toth, R., Madabhushi, A., “Multi-Feature Landmark-Free Active Appearance Models: Application to Prostate MRI Segmentation,” IEEE Transactions on Medical Imaging 31(8), pp. 1638-1650. June 2012.

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IMAGE REGISTRATION

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Prostatome™: Prostate Imaging Atlas via Anatomic Constrained Registration

Statistical Models for the Anatomic Structures:

- Prostate capsule- Central Gland- Peripheral Zone

Distribution of cancer:- High frequency- Medium- Low

M. Rusu, B. N. Bloch, C. C. Jaffe, N. M. Rofsky, E. M. Genega, R. E. Lenkinski, A. Madabhushi , Statistical 3D prostate imaging atlas construction via anatomically constrained registration, Accepted SPIE 2013

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A Statistical Deformation Model (SDM) based Regularizer for Non-rigid Image Registration: Application to registration of multimodal prostate MRI and histology

Moving ImageTemplate Image Moved Image (FFD) Moved Image (FFD+SDM)

• Regularizer needed in registration to allow for a physically meaning transformation.

• Here, we leverage knowledge of known, valid deformations to train a statistical deformation model (SDM)

• Harness this knowledge alongside FFD to provide an accurate overlay of histology on MRI. M

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tS. Babu Prabu,, R. Toth, A. Madabhushi, “A Statistical Deformation Model (SDM) based Regularizer for Non-rigid Image Registration: Application to registration of multimodal prostate MRI and histology,” Proc. SPIE, Medical Imaging: Digital Pathology, 2013.

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Incorporating the Whole-Mount Prostate Histology Reconstruction Program Histostitcher© into the Extensible

Imaging Platform (XIP) Framework• Histostitcher algorithm incorporated into

professional XIP software framework.• GPU rendering, “Google Maps” – like

zooming, scrolling

Previous version of Histostitcher© graphical user interface usable prototype developed using Matlab.

New version of Histostitcher© graphical user interface developed using XIP.

Toth, R., Chappelow, J., Kutter, O., Vetter, C., Russ, C., Feldman, M., Tomaszewski, J., Shih, N., Madabhushi, A., “Incorporating the Whole-Mount Prostate Histology Reconstruction Program Histostitcher© into the Extensible Imaging Platform (XIP) Framework,” SPIE Medical Imaging 8315, 2012.

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Fully-Automated T2 W MRI-TRUS FusionObjective: Registration of MRI-TRUS with no manual intervention

Module 1. MRI Prostate

Segmentation (a) Spatial Prior

Module 2. TRUS Probabilistic Model of Prostate Location

(b) Estimation of texture-base pixel-wise probability

Module 3. Register Model to Segmentation

(a) Affine Registration

(b) Elastic Registration

R. Sparks, E. Feleppa, N. Bloch, D. Barratt and A. Madabhushi. Fully Automated Prostate Magnetic Resonance Imaging and Transrectal Ultrasound Fusion via a Probabilistic Registration Metric. In Proc. SPIE 2012, in press.

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Learning-based Landmark Driven Image Registration

Tao Wan, B.Nicolas Bloch, and Anant Madabhushi, A Novel Point-based Nonrigid Image Registration Scheme Based on Learning Optimal Landmark Configurations, to appear in Proc. SPIE 2013.

The objective of this work is to develop an effective approach to estimate an optimal landmark configuration in order to driving point-based non-rigid image registration schemes.

(a) (b) (c)

The difference map between: (a) the original and deformed images, and (b), (c) the original and registered images using ICP-based, and FFD methods, respectively.

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Co-registration of MRI via a Learning Based Fiducial-driven registration (LeFiR) Scheme

A B C D E F

G H I J K L

(A)(B), (G)(H) pre- and post-treatment brain images, (C)(I) identified landmarks, (D)(J) uniformly spaced landmarks, and difference images between pre-and registered images using (E)(K) identified landmarks, (F)(L) uniformly spaced landmarks.

T.Wan, B.N.Bloch, A.Madabhushi, Learning based fiducial driven registration (LeFiR): Evaluating laser ablation changes for neurological applications, submitted to ISBI 2013.

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Mouse Lung Reconstruction from Groupwise Registration of Individual Histological Slices with Natural Gradient

Haibo Wang, etc., Mouse Lung Volume Reconstruction from Efficient Groupwise Registration of Individual Histological Slices with Natural Gradient, SPIE 2013.

Perform groupwise registration of individual histological slices.

Present a fast incremental Lucas-Kanade computation framework.

Integrate natural gradient for faster and more reliable convergence.

Fig. 1 Reconstructed volumes

Fig. 2 Convergence curves of registration approaches

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Multiscale, multimodal fusion of histological and MRI lung volumes for characterization of airways, M. Rusu, H. Wang, T. Golden, A. Gow, A. Madabhushi, Accepted SPIE 2013

Fusion of histological and MRI lung volumes

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MACHINE LEARNING

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Class-specific weighting for Markov random field estimation: Application to medical image segmentation, Medical Image Analysis. 2012 Dec;16(8):1477-89.

Class-Specific Weighting for Markov Random Field Estimation

Methods for varying the performance of MRFs is conspicuously absent from the literature. Thus, we introduced multiplicative weighted MAP estimation for MRFs.

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“Computer-aided Detection of Peripheral Zone and Central Gland Prostate Tumors on T2-weighted MRI”, RSNA, 2012

Comparing Classifier Schemes for CaP Detection from T2w MRI

Empirically compared 12 different classifier schemes for detecting prostate cancer on a per-voxel basis using textural representations of T2w MRI. Simple QDA classifier performed comparably to popular complex SVM classifier.

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Variable Ranking for PCA

• Quantify the contributions of individual, high dimensional features to a PCA embedding

• Multi-parametric MR imaging markers for prostate cancer:– Importance of ADC intensity, in contrast to T2w

intensity– Importance of Gabor texture features extracted from

ADC maps and T2w MRI– Importance of choline, polyamine, and citrate

metabolites measured on magnetic resonance spectroscopy

• Facilitates the use and interpretation of high dimensional features while avoiding the curse of dimensionality

Perform PCA

Compute PCA-VIP

Ginsburg, S, Tiwari, P, Kurhanewicz, J, Madabhushi, A. 2011. Variable ranking with PCA: Finding multiparametric MR imaging markers for prostate cancer diagnosis and grading. Workshop on Prostate Cancer Imaging: Computer-Aided Diagnosis, Prognosis, and Intervention (in conjunction with MICCAI). 6963:146-157.

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Statistical Shape Model of Manifolds

Objective: Accurately model manifold structure

b. Generate manifold for each fold c. Learn Modela. Divide dataset

into K folds

R. Sparks and A. Madabhushi. Gleason grading of prostate histology utilizing statistical shape model of manifolds (SSMM.) In Proc. SPIE 2012, 8315: 83151J-83151J13.

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MULTIMODAL DATA INTEGRATION AND TREATMENT EVALUATION

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Radiohistomorphometry™: Correlating DCE-MRI and Microvascular Parameters to Identify in vivo Markers for

Prostate Cancer Aggressiveness

CD31 stained histology quadrants

Stitched section with CaP annotations

DCE MRI

Vascular Feature Extraction

Correlation

Histo-MR registration DCE-MRI Kinetic Feature Extraction

Radio-histomorphometricAnalysis to screen forcandidate Imaging Markers

Singanamalli et al. To appear in SPIE Medial Imaging 2013

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Quantitative Evaluation of Treatment Related Changes on Multi-Parametric MRI after Laser Interstitial

Thermal Therapy of Prostate CancerPreliminary results of developing a quantitative framework to evaluate treatment-related changes post-LITT on a per-voxel basis (high resolution), via construction of an integrated MP-MRI signature

Above, checkerboard registration results showing alignment of multi-protocol MRI, pre-/post-LITT MRIRight, difference maps showing significant changes in MR parameters within LITT ablation zone

T2/DCE T2/DWI Pre/PostAblation zone T2 difference map

ADC difference map MP-MRI difference

“Quantitative Evaluation of Treatment Related Changes on Multi-Parametric MRI after Laser Interstitial Thermal Therapy of Prostate Cancer”, SPIE, 2013

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Objective: To quantitatively evaluate changes in MP-MRI imaging markers (T1-w, T2-w, T2-GRE, T2-FLAIR, and ADC) over the epileptogenic foci, pre- and post- laser interstitial thermal therapy (LITT).

Tiwari, P, et al. . Quantitative evaluation of multi-parametric MRI marker changes post laset-interstitial ablation therapy (LITT) for epilepsy, SPIE 2013.

MP-MRI for Evaluating Treatment Related Changes Post-LITT in Epilepsy

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COMPUTER AIDED DIAGNOSIS AND PROGNOSIS

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Pharmacokinetic Modeling for Prostate Cancer Localization• Iterative multiple reference

tissue method (IMRTM) for pharmacokinetic (PK) feature extraction from DCE MRI – Patient-specific values

for Ktrans (volume transfer constant) and ve(extravascular-extracellular volume fraction) are iteratively estimated for voxels in tissues A and B.

– No need for arterial input function

– Does not rely on population-based PK parameters

• Preliminary results shown on the right for a CG tumor

KtransGround truth ve

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Ground Truth – Study 1

Ground Truth – Study 2

Cancer classification – Study 1

Cancer classification – Study 2

Grade classification – Study 1

Grade classification – Study 2

P. Tiwari, A. Madabhushi et al. Medical Image Analysis (MeDiA) – In press.

Hierarchical Prostate Cancer Grade Classification

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Quantifying Local Heterogeneity via Morphologic Scale (MS) Distinguishing Tumoral Regions from Stromal Regions

Algorithm Output

The MS signature overlaid on a tumor regions in an (a) ovarian,(b) prostate H, (c)breast HE, and (d) prostate HE image, to be compared with the benign signatures in((i)-(l)), respectively. We can see that in the presented non-tumor regions ((i)-(l))the MS signature has fewer and smaller objects to obstruct its path, and thus therays are less tortuous, unlike in the respective tumoral regions ((a)-(d)). This allowsa supervised classifier to be able to separate the two classes.

Results

AUC

Data Type Breast Prostate HE Prostate H Ovarian

AUC +/- Range .80 +/- .01 .88 +/- .01 .87 +/- .02 .88 +/- .01

Matlab/C++/CPU

0.0058 sec/sample

Andrew Janowczyk, Sharat Chandran, and Anant Madabhushi. Quantifying local heterogeneity via morphologic scale: Distinguishing tumor from stroma. HIMA Workshop MICCAI, 2012.

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Evaluating the need for intensity artifact correction in MP-MRI for computerized detection of prostate cancer in vivo

Demonstrated that explicit correction of intensity inhomogeneity and drift in all 3 MP-MRI protocols (T2w, DCE, DWI) prior to combining them within a classification scheme will yield improved CaP detection accuracy in vivo.

“Evaluating the need for intensity artifact correction in MP-MRI for computerized detection of prostate cancer in vivo”, SIIM, 2013

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A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slices

Goal: Medulloblastoma Tumor Classification (Anaplastic vs Non-anaplastic) with good classification performance and visual interpretability.

c)

d)

a)

b)

e)

f)

(a) New histopathology images are represented as (b) a histogram of Bag of Features (BOF)according to (c) a dictionary of BOF learning using Haar-based descriptors of patches. (d) Aprobabilistic latent semantic analysis (pLSA) model is trained with image representation and theirclasses membership of training dataset in order to (e) predict the probability that a given imagebelongs to Anaplastic or Non-anaplastic type of Medulloblastoma tumor. Finally, (f) a visualprobabilistic map is provided according to regions in the image associated with each subtype.

Angel et al., “A Visual Latent Semantic Approach for Automatic Analysis and Interpretation of Anaplastic Medulloblastoma Virtual Slices”, MICCAI, 2012.

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Co-occurring Gland Tensors (CGT) in Localized Cluster Graphs for CaP Histology

CGTs describe the ability for glands to orient themselves with respect to prostate stroma

CaP Recurrence

No Recurrence

Calculate Gland Direction Establish Locality CGT

Lee et al. ISBI 2013 Under Review

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Cell Cluster Graph for Prediction of Biochemical Recurrence in Prostate Cancer Patients from Tissue Microarrays

Novel Cell Cluster graph (CCG) that can quantify tumor morphology

Extracted features from CCG can predict Biochemical recurrence in Prostate Cancer in 80 patients.

Ali, Sahirzeeshan, Veltri, Robert, Epstein, Jonathan, and Madabhushi, Anant “Cell Cluster Graph for Prediction of Biochemical Recurrence in Prostate Cancer Patients from Tissue Microarrays”, SPIE Medical Imaging, 2013.(In Press)

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Ali, Sahirzeeshan, Lewis, James, Madabhushi, Anant, J Use of quantitative histomorphometrics to classify disease progression in HPV-positive squamous cell carcinoma.J. Clin Oncol 30: 2012 (suppl 30; abstr 73)

Using Cell Cluster graphs to differentiate Progressors vs Non-Progressors in p16+ Oropharyangeal tumors

Use of Quantitative Histomorphometry to Classify Disease Progression in HPV-positive Squamous Cell Carcinoma

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INTERESTED IN JOINING CCIPD?

We are always looking for enthusiastic and motivated graduate, undergraduate students and research scientists.

If you think you would be a good fit for CCIPD, send over your complete CV and 3 representative publications to “anant.madabhushi” @ “case.edu”

Follow us on Twitter: @CCIPD_Case

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