Multiple Organ detection in CT Volumes - Week 2 Daniel Donenfeld

Contents ●SLIC segmentation work ●SLIC segmentation parameters ●Superpixel segmentation using optical flow ●Comparison

SLIC Segmentation ●Implemented C++ wrapper to use SLIC implementation in MATLAB ●Wrote matlab script to visualize results ●Qualitatively tested different supervoxel parameters o Compactness - area to perimeter ratio  Trade off between compactness and boundary adherence 1 o Number of SuperVoxels

SuperVoxel implementation using Optical Flow ●Tested method from: Spatio-Temporal Object Detection Proposals, Dan Oneata, Jerome Revaud, Jakob Verbeek, Cordelia Schmid, ECCV 2014 ●Generates superpixels, and connects them across frames using hierarchical clustering

SuperVoxel implementation using Optical Flow ●Uses SLIC to make superpixels ●Graph with: o Edges between neighboring superpixels  Weight is from edges in image, optical flow, color information o Edges between 2nd order neighbors  Weight is from optical flow, color information and penalty o Edges between temporally connected neighbors using flow information  Weight is from optical flow, color information o Hierarchical clustering using average linkage

SuperVoxel implementation using Optical Flow

SuperVoxel Comparison ●Running Time: SLIC is much faster ●Segmentation Quality: Method using optical flow has better boundaries o Not enough supervoxels for accurate organ over- segmentation o Hardware constraints (Temporary) - Unable to run highest level of hierarchical clustering, which will improve results

Next Week Plan ●Find optimal parameters for running INRIA method ●Use random forest on supervoxels to begin classification of supervoxels o Find the important features of the supervoxels ●Annotate CT scans with ground truth bounding boxes for heart, kidneys and liver

Sources ●Dan Oneata, Jerome Revaud, Jakob Verbeek, Cordelia Schmid. Spatio- Temporal Object De- tection Proposals. David Fleet; Tomas Pajdla; Bernt Schiele; Tinne Tuytelaars. ECCV European Conference on Computer Vision, Sep 2014, Zurich, Switzerland. Springer, 8691, pp ,. ●Radhakrishna Achanta, Appu Shaji, Kevin Smith, Aurelien Lucchi, Pascal Fua, and Sabine Süsstrunk, SLIC Superpixels Compared to State-of-the- art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 34, num. 11, p , May 2012.SLIC Superpixels Compared to State-of-the- art Superpixel Methods