1. Multiple Organ detection in CT Volumes - Week 3
Daniel Donenfeld

2. 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

3. 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

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

5. 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 th 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,

6. 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,

7. 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 IEEE 10th International Symposium on Biomedical Imaging.

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

9. 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