Abdominal muscle segmentation from CT using a convolutional neural network

Ka'Toria Edwards; Avneesh Chhabra; James Dormer; Phillip Jones; Robert D. Boutin; Leon Lenchik; Baowei Fei

doi:10.1117/12.2549406

28 February 2020 Abdominal muscle segmentation from CT using a convolutional neural network

Ka'Toria Edwards, Avneesh Chhabra, James Dormer, Phillip Jones, Robert D. Boutin, Leon Lenchik, Baowei Fei

Proceedings Volume 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging; 113170L (2020) https://doi.org/10.1117/12.2549406
Event: SPIE Medical Imaging, 2020, Houston, Texas, United States

Abstract

CT is widely used for diagnosis and treatment of a variety of diseases, including characterization of muscle loss. In many cases, changes in muscle mass, particularly abdominal muscle, indicate how well a patient is responding to treatment. Therefore, physicians use CT to monitor changes in muscle mass throughout the patient’s course of treatment. In order to measure the muscle, radiologists must segment and review each CT slice manually, which is a time-consuming task. In this work, we present a fully convolutional neural network (CNN) for the segmentation of abdominal muscle on CT. We achieved a mean Dice similarity coefficient of 0.92, a mean precision of 0.93, and a mean recall of 0.91 in an independent test set. The CNN-based segmentation method can provide an automatic tool for the segmentation of abdominal muscle. As a result, the time required to obtain information about changes in abdominal muscle using the CNN takes a fraction of the time associated with manual segmentation methods and thus can provide a useful tool in the clinical application.

Citation Download Citation

Ka'Toria Edwards, Avneesh Chhabra, James Dormer, Phillip Jones, Robert D. Boutin, Leon Lenchik, and Baowei Fei "Abdominal muscle segmentation from CT using a convolutional neural network", Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170L (28 February 2020); https://doi.org/10.1117/12.2549406

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