CSE 554: Geometric Computing for Biomedicine

CSE 554: Geometric Computing for Biomedicine

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CSE 554: Geometric Computing for Biomedicine

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1. Outline • Introduction to course • Mechanics • Mathematica demo

2. Outline • Introduction to course • Mechanics • Mathematica demo

3. Geometry • Greek word: Earth-measuring • One of the oldest sciences Chinese Chou Pei Suan Ching (500-200 BC) Euclid’s Element (300 BC)

4. Geometry • Greek word: Earth-measuring • One of the oldest sciences Newton’s Principia Mathematica (1687) Einstein’s General Relativity (1915)

5. Geometric Forms Curves Surfaces • Continuous geometry • Defined by mathematical functions • E.g.: parabolas, splines, subdivision surfaces • Discrete geometry • Disjoint elements with connectivity relations • E.g.: polylines, triangle surfaces, pixels and voxels Polyline Triangle surfaces (meshes) Pixels Voxels

6. Geometric Computing • Algorithms and data structures for (discrete) geometry • Creation • From 2D/3D images, from point clouds, by hand, etc. • Processing • De-noise, simplify, repair, transform, animate, etc. • Analysis • Geometric, topological, shape and physical properties

7. Applications Industrial design Cultural heritage Engineering simulation Geology Movie CG

8. Application: Biomedicine • Modeling biological structures as geometric forms • A spectrum of scales: organs, tissues, cells, molecules, etc. • With geometric representation, we can do • Visualization • Quantitative analysis • Simulation and interaction Human Virus Treatment planning Surgical simulation

9. This Course • Classical algorithms for geometric computing • Widely used for biomedical image analysis • Easy to understand, simple to implement

10. This Course • Working with biomedical imaging data • 2D: Light microscopy, slices of 3D images • 3D: Magnetic resonance imaging (MRI), Computed tomography (CT), Cryo-Electron Microscopy (Cryo-EM) Microscopy Cryo-EM CT

11. This Course • Creating, processing, deforming, and analyzing geometry Fair & Simplify Segment Extract boundary Shape analysis Align & Deform (Before) (After)

12. Beyond This Course • On-going research projects on biomedical modeling • Gorgon: shape analysis of proteins (Gorgon.wustl.edu) • Geneatlas: image-based queries in mouse brains (Geneatlas.org) • VolumeViewer: interactive 3D segmentation (Volumeviewer.cse.wustl.edu) • Research opportunities in the M&M lab • Biomedical modeling (Tao) • Image analysis (Robert, Tao) • Computer vision (Robert, Yasu) • Machine learning (Kilian) • Human computer interaction (Caitlin)

13. Outline • Introduction to course • Mechanics • Mathematica demo

14. Staff • Instructor: Tao Ju • Jolley 406 (taoju@cse.wustl.edu) • TA: • Michelle Vaughan (mavaugha@wustl.edu) • Ming Zou (mingzou.cn@gmail.com)

15. Prerequisites • Programming • Experienced in at least one of the major programming languages • C/C++, Java, Matlab, Python, etc. • CSE332 is strongly recommended • CS background • Basic data structures (e.g., queues, trees, hash tables) and algorithms • CSE241 is strongly recommended • Math • Linear algebra, elementary geometry

16. Overview • 2 meetings per week • Lectures on Tuesdays (Cupples II 200) • Labs on Thursdays (Whitaker 130) • 5+1 lab modules • 2 weeks for each module (1 week for Module 0) • Due and graded in lab on Thursdays. • 1 course project • Proposal due in November • Final presentation in December • Check out the calendar on course webpage No exams!

17. Lectures • Theory and algorithms • Algorithms are explained in depth, pseudo-code given when possible Example: • … • Repeat until Q is empty: • Pop a pixel x from Q. • For each unvisited object pixel y connected to x, add y to S, set its flag to be visited, and push y to Q. • Output S

18. Lab Modules • Algorithm prototyping (in Mathematica) • Step-by-step, easy to hard, 2D to 3D • Unit tests • Work individually Example:

19. Course Project • A working tool that solves an existing problem in biomedical research • Topics provided by the instructor or identified by students • Use your favorite programming language • Work in team or individually

20. Course Projects • Example projects (Fall 2013) (Ethan Green)

21. Course Projects • Example projects (Fall 2013) (Qiushi Li)

22. Course Projects • Example projects (Fall 2013)

23. Course Projects • Example projects (Fall 2013) (Omer Turgeman and Matan Ronen)

24. Grading • Lab modules: 75% (graded during Thursday labs) • Course project: 25% • Late policy • Late modules and project will earn at most 50% credit for the late part • Modules submitted later than the Tuesday following the due date will not be accepted. • Extensions will be given only under exceptional conditions, by written requests ahead of time.

25. Outline • Introduction to course • Mechanics • Mathematica demo

26. Action Items – This Week • Make sure you have a SEAS account • Check with the help desk at EIT in Lopata 4nd floor. • Get access to Mathematica • Available on all SEAS machines; installed freely on campus computers • Purchase for personal use for \$45 / semester • Module 0 is out today • I will give a quick tutorial and help you with it this Thursday • Due and graded next Thursday in lab (Sept. 4) • See you all on Thursday (Whitaker 130)!