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Methods In Medical Image Analysis

Spring 2013 BioE 2630 (Pitt) : 16-725 (CMU RI)

18-791 (CMU ECE) : 42-735 (CMU BME)

Dr. John Galeotti

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What Are We Doing?

Theoretical & practical skills in medical image analysisImaging modalitiesSegmentationRegistrationImage understandingVisualization

Established methods and current research

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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: 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) See website for HW1 details.

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Syllabus

On the course websitehttp://www.cs.cmu.edu/~galeotti/methods_course/

PrerequisitesVector calculusBasic probabilityKnowledge of C++ and/or PythonHelpful 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