Multiple Organ detection in CT Volumes Using Random Forests - Week 6 Daniel Donenfeld

Work This week Tested features on more samples 3D SIFT is not discriminative enough for our purposes. Began work on using distance priors

Results - Histogram Features Percent Liver Correct vs Number of Trees Percent Heart Correct vs Number of Trees Number of Trees Number of Trees

Results - Histogram Features Percent Left Kidney Correct vs Number of Trees Percent Right Kidney Correct vs Number of Trees Number of Trees Number of Trees

Results - 3DSIFT Features Classified every supervoxel as background Verified the classifier by training SVM on 3D SIFT features with same results Can test again after removing normalization steps Currently worse results than histogram features Have not yet re-tested 3D HOG

Distance Priors There are 50,000,000-100,000,000 voxels Using supervoxels search space is reduced to 3000 supervoxels Want to reduce search space further

Distance Priors We have prior information about the location of organs Human Anatomy dictates general location of organs within a person Just search the area where the organ is going to be located

Registering Images People are all different sizes Register images to single space Then there is a common set of coordinates

Image Registration and Atlas Used to normalize scans of a single patient Progression of COPD in the lungs1 Used global affine transform and B-splines Mean sum of squared distances similarity Both are tailored for intra-patient computations 1. Gorbunova, V., Lo, P., Ashraf, H., Dirksen, A., Nielsen, M., & Bruijne, M. (n.d.). Weight Preserving Image Registration for Monitoring Disease Progression in Lung CT. Lecture Notes in Computer Science Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 863-870.

Image Registration and Atlas Whole Heart Segmentation2 Locally Affine Registration Method Global affine is not accurate enough for segmentation Non-rigid transform could affect topology Abdominal Segmentation3 Mutual information similarity Warping transform - TPS Use of multiple atlases and confidence fusion4 2. Zhuang, X., Rhode, K., Arridge, S., Razavi, R., Hill, D., Hawkes, D., & Ourselin, S. (n.d.). An Atlas-Based Segmentation Propagation Framework Using Locally Affine Registration – Application to Automatic Whole Heart Segmentation. Lecture Notes in Computer Science Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008, 425-433. 3. Park, H., Bland, P., & Meyer, C. (n.d.). Construction of an abdominal probabilistic atlas and its application in segmentation. IEEE Transactions on Medical Imaging IEEE Trans. Med. Imaging, 483-492. 4. Acosta, O., Dowling, J., Drean, G., Simon, A., Crevoisier, R., & Haigron, P. (2013). Multi-Atlas-Based Segmentation of Pelvic Structures from CT Scans for Planning in Prostate Cancer Radiotherapy. Abdomen and Thoracic Imaging, 623-656. Mutual information - MI(F, M ) = H(F) + H(M ) - H(F, M)

Image Registration - Affine Before Registration After Registration

Image Registration