Yen Le Computation Biomedicine Lab Advisor: Dr. Kakadiaris

1. Automatic Multi-Region Segmentation Applied to Gene Expression Image from Mouse Brain Yen Le Computation Biomedicine Lab Advisor: Dr. Kakadiaris

2. Problem Statement • Problem Statement: Segmentation anatomical regions of mouse brain gene expression images (in 2D or 3D) • Data:In Situ Hybridization (ISH) images • Motivation: • Identify and associate the location and extent of expression of a gene in mouse brain image • Understand how genes regulate the biological process at cellular and molecular levels

3. Challenges • Large variations in boundary shape

4. Challenges (2) • Large variations in the shape of the anatomical regions

5. Challenges (3) • Large variations in intensity

6. Accomplishments to-date • 2D • Geometric model to image fitting methods • Image-to-image registration method • 3D • Descriptors for 3D landmark detection

7. 3D Dense Local Point Descriptors • Motivation • Need for anatomical landmarks • Need 3D local point descriptors which can: • Be computed fast at densely sampled points • Result in accurate landmark point detection

8. 3D Dense Local Point Descriptors (2) • DAISY3D and DAISYDO • Extended from DAISY descriptor • Faster than SIFT-3D, n-SIFT at densely sampled points • Good for landmark detection on gene expression images • DAISY3D vs. DAISYDO • DAISYDO requires less memory than DAISY3D • DAISYDO is faster • Comparable performance

9. 3D Dense Local Point Descriptors (3) DAISY’s configuration Configuration Forming DAISY feature vector Forming DAISYDO feature vector

10. Computational Time All methods are implemented in C++ and run in single core 1.86 GHz CPU

11. Memory Requirement Memory requirements for a sample volume of size 100x100x100

12. Performance Evaluation • Detected landmarks: voxels having the minimum -distance between its descriptor and the descriptor of referenced landmark Mean error (in voxels) for landmark detection in gene expression image

13. Publications Refereed Journal Articles Yen H. Le, U. Kurkure, I. A. Kakadiaris, “Dense Local Point Descriptors for 3D Images,” Pattern Recognition (Submitted). U. Kurkure, Yen H. Le, N. Paragios, J. Carson, T. Ju, I. A. Kakadiaris, “Landmark-Constrained Deformable Image Registration of Gene Expression Images for Atlas Mapping,” NeuroImage, Elsevier Science (Submitted). Refereed Conference Articles Yen H. Le, U. Kurkure, N. Paragios, J. P. Carson, T. Ju, and I. A. Kakadiaris, “Similarity-based appearance prior for fitting subdivision mesh in gene expression image,” IEEE Computer Vision and Pattern Recognition 2012 (Submitted). U. Kurkure, Y. H. Le, N. Paragios, J. P. Carson, T. Ju, and I. A. Kakadiaris. “Landmark/image-based deformable registration of gene expression data,” In Proc. IEEE Computer Vision and Pattern Recognition, pages 1089–1096, Colorado Springs, CO, Jun. 21-23 2011. U. Kurkure, Y. H. Le, N. Paragios, J. Carson, T. Ju, and I. A. Kakadiaris, Nov. 6-13 2011, “Markov random field-based fitting of a subdivision-based geometric atlas,” In: Proc. IEEE International Conference on Computer Vision. Barcelona, Spain, pp. 2540–2547.

14. Thank You for your attention!