1. Multiple Organ detection in CT Volumes Using Random Forests - Week 7
Daniel Donenfeld

2. Results
All new features were not discriminative enough due to small ratio of positive to negative data
Tried training and testing on only ground truth positive examples
Tested features on patches first to verify that features were working
Wrote 3D haar-like code
Tested SPOT on patient

3. Large Amount of Background
There are 3000 supervoxels in each patient
Around 3 percent of those are organs
Huge amount of background supervoxels trained classifier to recognize everything as background

4. 3D Haar-Like features
Paper on using 3D haar-like features: Cui, X., Liu, Y., Shan, S., Chen, X., & Gao, W. 3D Haar-Like Features for Pedestrian Detection. Multimedia and Expo, 2007 IEEE International Conference on.
Described extending haar to 3D
Implemented the method they proposed with filters on right

5. Results No Background- Histogram

6. Results No Background- Gradient

7. Results No Background - GLCM

8. Random Sample BG - Histogram
Randomly sampled 1000 background supervoxels

9. Next Week Plans
Investigate using SPOT and confidence fusion to improve results from supervoxels
Find optimal sampling amount of background
Continue tuning feature vectors