Colitis detection on abdominal CT scans by rich feature hierarchies

Jiamin Liu; Nathan Lay; Zhuoshi Wei; Le Lu; Lauren Kim; Evrim Turkbey; Ronald M. Summers

doi:10.1117/12.2217681

24 March 2016 Colitis detection on abdominal CT scans by rich feature hierarchies

Jiamin Liu, Nathan Lay, Zhuoshi Wei, Le Lu, Lauren Kim, Evrim Turkbey, Ronald M. Summers

Proceedings Volume 9785, Medical Imaging 2016: Computer-Aided Diagnosis; 97851N (2016) https://doi.org/10.1117/12.2217681
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Colitis is inflammation of the colon due to neutropenia, inflammatory bowel disease (such as Crohn disease), infection and immune compromise. Colitis is often associated with thickening of the colon wall. The wall of a colon afflicted with colitis is much thicker than normal. For example, the mean wall thickness in Crohn disease is 11-13 mm compared to the wall of the normal colon that should measure less than 3 mm. Colitis can be debilitating or life threatening, and early detection is essential to initiate proper treatment. In this work, we apply high-capacity convolutional neural networks (CNNs) to bottom-up region proposals to detect potential colitis on CT scans. Our method first generates around 3000 category-independent region proposals for each slice of the input CT scan using selective search. Then, a fixed-length feature vector is extracted from each region proposal using a CNN. Finally, each region proposal is classified and assigned a confidence score with linear SVMs. We applied the detection method to 260 images from 26 CT scans of patients with colitis for evaluation. The detection system can achieve 0.85 sensitivity at 1 false positive per image.

Citation Download Citation

Jiamin Liu, Nathan Lay, Zhuoshi Wei, Le Lu, Lauren Kim, Evrim Turkbey, and Ronald M. Summers "Colitis detection on abdominal CT scans by rich feature hierarchies", Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97851N (24 March 2016); https://doi.org/10.1117/12.2217681

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