Multiple Organ detection in CT Volumes Using Random Forests dbd64@cornell.edu Cornell University shussein@knights.ucf.edu University of Central Florida Results Discussion Materials & Methods Problem and Motivation Organ localization is the process of finding the location and extent of an organ in a medical image. Methods Motivation Useful for segmentation tasks Anomaly detection, e.g. Tumors Fat quantification Better medical image databases Selectively load region of interest to reduce image size Features Tested Histogram Gradient Histogram Haar 3D SIFT Gray Level Co-Occurrence Matrix Features Non-local patch Features Issues There was significantly more background than relevant organ Resolved by adding weights to the classifier to rely more on the organs Supervoxels Supervoxel segmentation is an over-segmentation technique Reduce the search space A CT scan can have 50 to 100 million voxels Can over-segment into 3000 supervoxels Previous papers have used SLIC We used a method which used SLIC superpixels, edges, and optical flow to compute supervoxels Qualitatively better edge adhesion then SLIC supervoxels 3D Haar feature masks used Results Thank you to the NSF for funding the REU program for the University of Central Florida. Also, thanks to Dr. Shah, Dr. Bagci, and Dr. Lobo for overseeing the program. Acknowledgements Computationally expensive supervoxel and feature extraction Limitations Future Work Organ Classifications: Dark Blue: Background, Yellow: Heart, Light Blue: Liver, Red: Kidney Segment into supervoxels Extract hand-crafted features Classify supervoxels Smooth Data Return bounding boxes Location of samples around super voxel center Small sample of a random forest, showing two trees which contribute to the classifications State of the Art: Regression Forests for Efficient Anatomy Detection and Localization in CT Studies Fast algorithm: ~6 seconds Error approximately halved over previous state of the art atlas based registration Acts on each voxel: each one predicts every voxel Advantages: Significantly smaller search space Each supervoxel has much more information than a single voxel Haar Features gave best results Misclassification: Near the organs, many supervoxels are misclassified Bounding volume contains non-organ supervoxels Organ Classifications: Dark Blue: Background, Yellow: Heart, Light Blue: Liver, Red: Kidney Haar with 20 train patients, 10 test patients Use Confidence fusion to smooth classification results Reduce the search space using hierarchical anatomical structure The shape of supervoxels shown across four slices of a patient. The heart can be seen in the center, which have good adherence to the boundaries of organs. The lungs can be seen on either side in dark purple and green Comparison of Haar features with different extensions