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Proceedings Paper

Abdominal muscle segmentation from CT using a convolutional neural network
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Paper 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.

Paper Details

Date Published: 28 February 2020
PDF: 9 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170L (28 February 2020); doi: 10.1117/12.2549406
Show Author Affiliations
Ka'Toria Edwards, The Univ. of Texas at Dallas (United States)
Avneesh Chhabra, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
James Dormer, The Univ. of Texas at Dallas (United States)
Phillip Jones, The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)
Robert D. Boutin, Univ. of California, Davis (United States)
Leon Lenchik, Wake Forest Univ. School of Medicine (United States)
Baowei Fei, The Univ. of Texas at Dallas (United States)
The Univ. of Texas Southwestern Medical Ctr. at Dallas (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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