1. Multiple Organ detection in CT Volumes Using Random Forests
Cornell University
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