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Yen LeComputation Biomedicine Lab

Advisor: Dr. Kakadiaris

Automatic Multi-Region Segmentation Applied to Gene Expression Image from Mouse

Brain

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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

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Challenges

• Large variations in boundary shape

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• Large variations in the shape of the anatomical regions

Challenges (2)

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• Large variations in intensity

Challenges (3)

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Accomplishments to-date

• 2D– Geometric model to image fitting methods– Image-to-image registration method

• 3D– Descriptors for 3D landmark detection

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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

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• 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

3D Dense Local Point Descriptors (2)

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3D Dense Local Point Descriptors (3)

DAISY’s configuration Configuration

Forming DAISY feature vectorForming DAISYDO feature vector

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Computational Time

All methods are implemented in C++ and run in single core 1.86 GHz CPU

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Memory Requirement

Memory requirements for a sample volume of size 100x100x100

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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

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PublicationsRefereed 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 ArticlesYen 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.

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Thank You for your attention!