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Lecture 1: Introduction to Medical Image Computing 1 MEDICAL IMAGE COMPUTING (CAP 5937)- SPRING 2016 LECTURE 1: Introduction Dr. Ulas Bagci HEC 221, Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, FL 32814. [email protected] or [email protected] 2 • This is a special topics course, offered for the first time in UCF. Lorem Ipsum Dolor Sit Amet CAP5937: Medical Image Computing 3 • This is a special topics course, offered for the first time in UCF. • Lectures: Mon/Wed, 10.30am- 11.45am Lorem Ipsum Dolor Sit Amet CAP5937: Medical Image Computing 4 • This is a special topics course, offered for the first time in UCF. • Lectures: Mon/Wed, 10.30am- 11.45am • Office hours: Lorem Ipsum Dolor Sit Amet Mon/Wed, 1pm- 2.30pm CAP5937: Medical Image Computing 5 • 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 Lorem Ipsum Dolor Sit Amet • No textbook is required, materials will be provided. CAP5937: Medical Image Computing 6 Image Processing Computer Vision Medical Image Imaging Computing Sciences (Radiology, Biomedical) Machine Learning 7 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. 8 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. 9 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! 10 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 11 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%) 12 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 13 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, … 14 Required skill set • Basic programming experience (any language is fine) • Linear Algebra/Matrix Algebra • Differential Equations • Basic Statistics 15 Biomedical Images • (Bio)medical images are different from other pictures 16 Biomedical Images • (Bio)medical images are different from other pictures – They depict distributions of various physical features measured from the human body (or animal). 17 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 18 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 19 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 20 • Thank you for your attention!.
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