Fast Algorithm for Probabilistic Bone Edge Detection

1. Fast Algorithm for Probabilistic Bone Edge Detection FAPBED Danilo Scepanovic Josh Kirshtein Mentor: Ameet Jain

2. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

3. Goal • Given an arbitrary CT image, quickly and automatically segment bone edges using information-theoretic methods • Assign each voxel a probability of being a bone surface point • Model the probabilistic surface • Final goal of registering US scans to the CT volume

4. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

5. Problem • CT images contain incomplete / extraneous / confusing information • No information about relative location • within the body • Soft tissue • Multiple bones of variable density • Air pockets • Large image size makes quickprocessing difficult http://scw.asahi-u.ac.jp/~kawamata/images/case1/

6. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

7. Relevance and Importance • Many applications • Useful for building statistical models • Registration (US to CT) • Pre-operative planning • Aid in fast trauma diagnosis • Research Possibilities • - Expandable to soft tissue modeling • - Methods applicable for segmentation of US images • - Speed and accuracy improvements are always possible http://www.gemedicalsystems.com/rad/ct/images/med/qxi/denta_lsa_denta8.jpg

8. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

9. Deliverables • Minimum • Probabilistic bone edge detection algorithm • Functional implementation of algorithm • No speed claims • Expected • More sophisticated algorithm involving neural networks / AI if applicable • Expected accuracy with known models ~ 90% • Sub 120 sec execution time for pelvic CT segmentation • Maximum • Sub 60 sec execution time with ~ 98% accuracy • Rapid, non-iterative US to CT registration

10. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

11. Approachbasic • Basic Algorithm Development • Identify characteristics of bone portions of CT images using thresholding • Label all voxels with probability value of being bone • Keep in mind to identify characteristics suitable for self-learning algorithms • Ex. Compare frequency of voxels within certain range to the number of those voxels that are actually bone

12. Approachbasic(Sample Algorithm) • = density of voxel • lies in the identified range of densities • Number of voxels in range • Number of bone voxels in range • Actual surface identification by algorithm would be dependant on surrounding voxel probabilities

13. Approachbasic • Functional Implementation • - Based on individual probability values, identify bone portions of CT • Isolate bone voxels from total image • Remove ‘internal’ high probability voxels • Reconstruct probabilistic bone surface • Compare to pre-segmented images for estimation of accuracy of algorithm • Concentrate on making it work, no goal of making it speedy at this time

14. Approachimproved • Improved Algorithm • Utilize characteristics of bones observed while performing thresholding to form basis for neural network detection algorithm • Supervised • Unsupervised • Other AI methods • Sample properties • Actual voxel density value • Characteristics of surrounding voxels • Characteristics of surface (rough vs smooth) • Closed contours

15. Approachimproved • Improved Functional Implementation • Based on probabilities assigned by neural network • Isolate high probability voxels from image • Remove ‘internal’ high probability voxels • Reconstruct probabilistic bone surface • Compare to pre-segmented images for estimation of algorithm accuracy and for error back propagation to neural network • Again, no claims as to performance speed

16. Approachadvanced • Advanced Functional Implementation • Improve and enhance algorithm to run more efficiently and quickly • Update network structure or implement other learning algorithm http://www.gemedicalsystems.com/rad/ct/images/med/qxi/denta_lsa_denta8.jpg http://www.gemedicalsystems.com/rad/ct/images/med/cte/cte_lspine_3d1.jpg

17. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

18. Key Dates • 2/27 - Completed Background Reading / Image familiarization • 3/5 – Basic thresholding algorithm completed • 3/12 – Basic implementation completed • 3/19 – Neural network training properties identified, begin work on neural network

19. Key Dates cntd • 3/26 – First attempt at neural network implementation • 4/2 – Errors / shortfalls from first attempt identified and repaired in more advanced implementation • 4/9 – Evaluation of speed-limiting steps completed and speed-ups investigated • 4/16 – Speed-ups implemented and tests begun with new datasets

20. Key Dates cntd • 4/23 – 4/30 Evaluation of tests with new datasets and fine tuning of algorithm completed. Tweak algorithm as necessary and continue testing

21. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

22. Dependencies • More references (Ofri) • CT images from CISST ERC sources (Anton, Ming) • A meeting with Ameet • Work Allocation • All work will be done jointly

23. Presentation Outline • Goal • Problem • Relevance and Importance • Deliverables • Approach • Basic • Improved • Advanced • Key Dates • Dependencies • Work Allocation • Background Readings • Web References

24. Background Reading • Greenspan, M.A, Yurick, M., "An Approximate K-D Tree Search for Efficient ICP", 3DIM03: 4th International Conference on 3-D Digital Imaging and Modeling, Banff, Alberta, Canada, Oct. 6-10, 2003. • X.M. Pardo, D. Cabello and J.Heras “An Integration Scheme for Biomedical CT Image Segmentation”, Journal of Computing and Information Technology (CIT). Vol.7, nº. 4, pp. 295-309,1999 • C-F Westin, A. Bhalerao, H. Knutsson and R. Kikinis, “Using Local 3D Structure for Segmentation of Bone from Computer Tomography Images” http://splweb.bwh.harvard.edu:8000/pages/ppl/westin/papers/cvpr97/ • Ping He, Jun Zheng, “SEGMENTATION OF TIBIA BONE IN ULTRASOUND IMAGESUSING ACTIVE SHAPE MODELS”, Proceedings – 23rd Annual Conference – IEEE/EMBS Oct.25-28, 2001, Istanbul, TURKEY (http://www.cs.wright.edu/~phe/Research/EMBS-01.pdf) • More to come

25. Further Web References http://www.ece.queensu.ca/hpages/faculty/greenspan/Pubs.htm http://www.ece.queensu.ca/hpages/faculty/greenspan/papers/GreYur03.pdf http://www.cs.queensu.ca/home/ellis/research/Fresearch.htm http://www.nlm.nih.gov/research/visible/vhp_conf/dance/part1.htm http://www.imp.uni-erlangen.de/RSNA2001/IMP_Presentations_RSNA2001- Dateien/RSNA2001_SS642_screen.pdf http://www.cs.bu.edu/fac/betke/women/CREW-2002-2003/kenda/