Multiple Organ detection in CT Volumes - Week 3 Daniel Donenfeld

Supervoxels Set up code on a lab machine to run all the samples Flow computation working correctly Bug in the code for edge detection Have not been able to run supervoxel segmentation, need edges first

Annotations Have begun annotating scans There are 200 full ct scans, we need ground truth bounding volumes for heart, kidneys, liver How to annotate? Create a rough volume Go through slice by slice in each direction to ensure it is tight, and contains the entire organ

Classifying Supervoxels Goal: Classify each supervoxel as organ or background Determine the features to use Setup Random forest in matlab

Features to Use Simplest features are histogram based1 Mean Variance Skewness Kurtosis Minimum Maximum Also use gradient histogram2 1. Mansoor, A., Bagci, U., & Mollura, D. (n.d.). Near-optimal keypoint sampling for fast pathological lung segmentation. 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2. Wang, H., & Yushkevich, P. (n.d.). Multi-atlas Segmentation without Registration: A Supervoxel-Based Approach. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2013 Lecture Notes in Computer Science, 535-542.

Features to Use Gray-level co-occurrence matrix (GLCM) are a popular choice How many times does a pair of pixels at a specific displacement from each other appear? energy, entropy, correlation, contrast, variance, sum of mean, inertia, cluster shade, cluster tendency, homogeneity, maximal probability, and inverse variance1,3 3. Liu, J., Pattanaik, S., Yao, J., Turkbey, E., Zhang, W., Zhang, X., & Summers, R. (n.d.). Computer aided detection of epidural masses on computed tomography scans. Computerized Medical Imaging and Graphics, 606-612.

Features to Use Texture map features - Gabor filters4 Angles of 0◦ , 45◦ , 90◦ , 135◦ Scales of 1 and 0.5 4. Mahapatra, D. (n.d.). Graph cut based automatic prostate segmentation using learned semantic information. 2013 IEEE 10th International Symposium on Biomedical Imaging.

Random Forest Work Wrote function to extract labels from supervoxel output Primitive feature extractor for mean and center of supervoxel

Next Week Plan Get Supervoxels for all samples Start with annotated samples Continue annotation of samples Write/use and test feature extraction Optimize parameters for random forest