A Deep Learning Model to Automate Skeletal Muscle Area Measurement on Computed Tomography Images
Kaushalya C Amarasinghe, Jamie Lopes, Julian Beraldo, Nicole Kiss, Nicholas Bucknell, Sarah Everitt, Price Jackson, Cassandra Litchfield, Linda Denehy, Benjamin J Blyth, Shankar Siva, Michael MacManus, David Ball, Jason Li, Nicholas Hardcastle
FRONTIERS IN ONCOLOGY | FRONTIERS MEDIA SA | Published : 2021
Background: Muscle wasting (Sarcopenia) is associated with poor outcomes in cancer patients. Early identification of sarcopenia can facilitate nutritional and exercise intervention. Cross-sectional skeletal muscle (SM) area at the third lumbar vertebra (L3) slice of a computed tomography (CT) image is increasingly used to assess body composition and calculate SM index (SMI), a validated surrogate marker for sarcopenia in cancer. Manual segmentation of SM requires multiple steps, which limits use in routine clinical practice. This project aims to develop an automatic method to segment L3 muscle in CT scans. Methods: Attenuation correction CTs from full body PET-CT scans from patients enrolled..View full abstract
This work was supported by the Peter MacCallum Cancer Centre Foundation.