MEDICAL IMAGE COMPUTING (CAP 5937)- SPRING 2016

LECTURE 1: Introduction

Dr. Ulas BagciHEC 221, Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, FL 32814.bagci@ucf.edu or bagci@crcv.ucf.edu

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• This is a special topics course, offered for the first time in UCF.

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CAP5937: Medical Image Computing

Lorem Ipsum Dolor Sit Amet

• This is a special topics course, offered for the first time in UCF.

• Lectures: Mon/Wed, 10.30am-11.45am

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CAP5937: Medical Image Computing

Lorem Ipsum Dolor Sit Amet

• This is a special topics course, offered for the first time in UCF.

• Lectures: Mon/Wed, 10.30am-11.45am

• Office hours: Mon/Wed, 1pm-2.30pm

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CAP5937: Medical Image Computing

Lorem Ipsum Dolor Sit Amet

• This is a special topics course, offered for the first time in UCF.

• Lectures: Mon/Wed, 10.30am-11.45am

• Office hours: Mon/Wed, 1pm-2.30pm

• No textbook is required, materials will be provided.

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CAP5937: Medical Image Computing

Lorem Ipsum Dolor Sit Amet

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Medical Image

Computing

Image Processing Computer

Vision

Machine Learning

Imaging Sciences

(Radiology, Biomedical)

Motivation• Imaging sciences is experiencing a tremendous

growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth.

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Motivation• Imaging sciences is experiencing a tremendous

growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth.

• Biomedical imaging and its analysis are fundamental to (1) understanding, (2) visualizing, and (3) quantifying information.

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Motivation• Imaging sciences is experiencing a tremendous

growth in the U.S. The NYT recently ranked biomedical jobs as the number one fastest growing career field in the nation and listed bio-medical imaging as the primary reason for the growth.

• Biomedical imaging and its analysis are fundamental to (1) understanding, (2) visualizing, and (3) quantifying information.

• This course will mostly focus on analysis of biomedical images, and imaging part will be briefly taught!

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Syllabus• Basics of Radiological Images, Imaging, and Their

Clinical Use– X-Ray, CT, MRI, fMRI, DTI, DWI, PET, dPET, PET/CT,

MRI/PET,…• Image Enhancement and Pre-processing– Spatial and Frequency Domain Filtering

• Medical Image Registration/Alignment– Atlas construction, disease tracking, severity analysis,…

• Medical Image Segmentation– Extraction of object information, volumetry, morphometry,..

• Medical Image Visualization• Machine Learning for Medical Imaging

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Syllabus• Grading:– 1 Mid-term exam at the classroom, written (20%)– 3 Programming Assignments (each 10%, total 30%)• ITK/VTK packages should be used• ITK and VTK provide necessary codes/libraries for medical

image processing and analysis. • C/C++ or Python can be used and call ITK/VTK functions• In-class collaboration is encouraged, but individual submission

is required.– 1 Individual Project (50%)• Will be selected from a list of projects• A short presentation (15%), coding/method (25%), results

(10%)

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Optional Reading List• Image Processing, Analysis, and Machine Vision. M. Sonka, V.

Hlavac, R. Boyle. Nelson Engineering, 2014.• Level-set Methods, by J. A. Sethian, Cambridge University Press.• Visual Computing for Medicine: Theory, Algorithms, and

Applications. B. Preim, C. Botha. Morgan Kaufmann, 2013.• Medical Image Registration. J. Hajnal, D. Hill, D. Hawkes (eds).

CRC Press, 2001.• Pattern Recognition and Machine Learning. C. Bishop. Springer,

2007.• Insight into Images: Principles and Practice for Segmentation,

Registration and Image Analysis, Terry S. Yoo (Editor) (FREE)• Algorithms for Image Processing and Computer Vision, J. R. Parker• Medical Imaging Signals and Systems, by Jerry Prince & Jonathan

Links, Publisher: Prentice Hall

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Conferences and Journals to Follow• The top-tier conferences (double blind, acceptance rates are below

25%, high quality technical articles):– MICCAI (medical image computing & computer assisted intervention)– IPMI (Information Processing in Medical Imaging)– Other conferences: IEEE ISBI, EMBC and SPIE Med Imaging– Clinical Conferences: RSNA (>65.000 attendances), ISMRM, SNM

• The top-tier technical journals:– IEEE TMI, TBME, PAMI, and TIP– Medical Image Analysis, CMIG, and NeuroImage

• The top-tier clinical journals relevant to MIC:– Radiology, Journal of Nuclear Medicine, AJR, Nature Methods, Nature

Medicine, PlosOne, …

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Required skill set• Basic programming experience (any language is fine)• Linear Algebra/Matrix Algebra• Differential Equations• Basic Statistics

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Biomedical Images• (Bio)medical images are different from other

pictures

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Biomedical Images• (Bio)medical images are different from other

pictures– They depict distributions of various physical features

measured from the human body (or animal).

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Biomedical Images• (Bio)medical images are different from other

pictures– They depict distributions of various physical features

measured from the human body (or animal body).• Analysis of biomedical images is guided by very

specific expectations

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Biomedical Images• (Bio)medical images are different from other pictures– They depict distributions of various physical features

measured from the human body (or animal).

• Analysis of biomedical images is guided by very specific expectations– Automatic detection of tumors, characterizing their types,– Measurement of normal/abnormal structures,– Visualization of anatomy, surgery guidance, therapy

planning, – Exploring relationship between clinical, genomic, and

imaging based markers

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Free Software to Use in this course• ImageJ (and/or FIJI)• ITK-Snap• SimpleITK• MITK• FreeSurfer• SLICER• OsiriX• An extensive list of software: www.idoimaging.com and

blue: will be frequently used in this course

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• Thank you for your attention!

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