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Methods In Medical Image Analysis. Spring 2014 BioE 2630 (Pitt) : 16-725 (CMU RI) 18-791 (CMU ECE) : 42-735 (CMU BME) Dr. John Galeotti. What Are We Doing?. Theoretical & practical skills in medical image analysis Imaging modalities Segmentation Registration Image understanding - PowerPoint PPT Presentation
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The content of these slides by John Galeotti, © 2008 to 2017 Carnegie Mellon University (CMU), was made possible in part by NIH NLM contract# HHSN276201000580P, and is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/ or send a letter to Creative Commons, 171 2nd Street, Suite 300, San Francisco, California, 94105, USA. Permissions beyond the scope of this license may be available either from CMU or by emailing [email protected] most recent version of these slides may be accessed online via http://itk.galeotti.net/
Methods In Medical Image Analysis
Spring 201716-725 (CMU RI) : BioE 2630 (Pitt)
Dr. John Galeotti
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What Are We Doing?
Theoretical & practical skills in medical image analysisImaging modalitiesSegmentationRegistrationImage understandingVisualization
Established methods and current researchFocus on understanding & using algorithms
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Why Is Medical Image Analysis Special?
Because of the patientComputer Vision:
Good at detecting irregulars, e.g. on the factory floorBut no two patients are alike—everyone is “irregular”
Medicine is warRadiology is primarily for reconnaissance Surgeons are the marines Life/death decisions made on insufficient information
Success measured by patient recoveryYou’re not in “theory land” anymore
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What Do I Mean by Analysis?
Different from “Image Processing”Results in identification, measurement, &/or judgment
Produces numbers, words, & actionsHoly Grail: complete image understanding automated within a computer to perform diagnosis & control robotic intervention
State of the art: segmentation & registration
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SegmentationLabeling every voxelDiscrete vs. fuzzyHow good are such labels?
Gray matter (circuits) vs. white matter (cables).Tremendous oversimplification
Requires a model
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Registration
Image to Imagesame vs. different imaging modalitysame vs. different patienttopological variation
Image to Modeldeformable models
Model to Modelmatching graphs
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Visualization
Visualization used to mean to picture in the mind.Retina is a 2D deviceAnalysis needed to visualize surfacesDoctors prefer slices to renderingsVisualization is required to reach visual cortexComputers have an advantage over humans in 3D
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Model of a Modern Radiologist
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How Are We Going to Do This?
The Shadow ProgramObserve & interact with practicing radiologists and
pathologists at UPMCProject oriented
C++ &/or Python with ITK New ITKv4!
National Library of Medicine Insight ToolkitA software library developed by a consortium of
institutions including CMU and UPittOpen source Large online communitywww.itk.org
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The Practice of Automated Medical Image Analysis
A collection of recipes, a box of toolsEquations that function: crafting human thought. ITK is a library, not a program.
Solutions:Computer programs (fully- and semi-automated).Very application-specific, no general solution. Supervision / apprenticeship of machines
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Who Are We?
Personal introductionsNameAcademic Background (ECE, Biology, etc.)Research InterestWhy you’re here
Homework 1: after we get a TA, I’ll have you email the TA/grader & myself the requested info about yourself, and a photo. (photo is optional, but requested; please crop to your head and shoulders) Details will be posted on the website
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Syllabus
On the course websitehttp://www.cs.cmu.edu/~galeotti/methods_course/
PrerequisitesVector calculusBasic probabilityKnowledge of C++ and/or Python Including command-line usage and command-line
argument passing to your codeHelpful but not required:Knowledge of C++ templates & inheritance
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Class Schedule
Comply with Pitt & CMU calendarsOnline and subject to changeBig picture:
Background & reviewFundamentalsSegmentation, registration, & other fun stuffMore advanced ITK programming constructsReview scientific papersStudent project presentations
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Requirements and Grading
Attendance: Required (quizzes)Quizzes: 20%
Lowest 2 droppedHomework: 30%Shadow Program: 10%Final Project: 40%
15% presentation25% code
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Textbooks
Required: Machine Vision, Wesley E. Snyder & Hairong Qi
Recommended: Insight into Images: Principles and Practice for Segmentation, Registration and Image Analysis, Terry S. Yoo (Editor)
Others (build your bookshelf)
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Superior = headInferior = feet
Anterior = frontPosterior = back
Proximal = centralDistal = peripheral
Anatomical Axes