Multiple Organ detection in CT Volumes Using Random Forests - Week 9

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Multiple Organ detection in CT Volumes Using Random Forests - Week 9 Daniel Donenfeld

Results Added Non-local patch information to improve classification results Improved the results minimally due to large amount of background Added weights to the data in training the random forest Weight smaller organs more heavily

Non-local Patch Information Generate points surrounding the center of the supervoxel At each point get mean of a small patch This encodes information on relative positions of organs Dang, K., Yuan, J., & Tiong, H. (2013). Voxel labelling in CT images with data-driven contextual features. 2013 IEEE International Conference on Image Processing.

Data Weights Give each data point a weight First assigned class weight (percent of class volume)-1 Did not improve results Manually choose weights Big improvement in results

Histogram

Gray Level Co-Occurrence Matrix

3D SIFT

Haar

Future Plans Focus on Haar Features Best results of the four features Test different parameters for non-local patch information Number of patches to sample, radius to sample Optimize weights on data Investigate CRF/MRF Extract bounding boxes