S029 - 3D Body Composition Segmentation in Abdomen and Pelvis CT using Subdivided Labels and Random Patch

Minyoung Kim, Ji-Won Kwon, Kwang Suk Lee, Taehoon Shin

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The distribution and volume of fat and muscle in APCT play an important role as a biomarker. In this study, APCT data from 200 individuals who underwent health screening was labeled into three classes of fat and four classes of muscle. Based on this labeling, 3D patch-wise segmentation was performed on the whole abdomen and pelvic scan images. The test results showed an overall class average of 0.89 DSC. This study conducted 3D whole-abdomen body composition segmentation using a total of eight segmented body composition labels including the background and verified its feasibility using random patches effective for the data and task.
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Schedule: Wednesday, July 12: Posters — 10:15–12:00 & 15:00–16:00
Poster location: W29