A comparative study of two automated solutions for cross‐sectional skeletal muscle measurement from abdominal computed tomography images - Charrière, Katia; Boulouard, Quentin; Artemova, Svetlana; Vilotitch, Antoine; Ferretti, Gilbert R.; Bosson, Jean‐Luc; Moreau‐Gaudry, Alexandre; Giai, Joris; Fontaine, Eric; Bétry, Cécile (Medical physics (Lancaster), 2023)
Background Measurement of cross‐sectional muscle area (CSMA) at the mid third lumbar vertebra (L3) level from computed tomography (CT) images is becoming one of the reference methods for sarcopenia diagnosis. However, manual skeletal muscle segmentation is tedious and is thus restricted to research. Automated solutions are required for use in clinical practice. Purpose The aim of this study was to compare the reliability of two automated solutions for the measurement of CSMA. Methods We conducted a retrospective analysis of CT images in our hospital database. We included consecutive individuals hospitalized at the Grenoble University Hospital in France between January and May 2018 with abdominal CT images and sagittal reconstruction. We used two types of software to automatically segment skeletal muscle: ABACS, a module of the SliceOmatic software solution “ABACS‐SliceOmatic,” and a deep learning‐based solution called “AutoMATiCA.” Manual segmentation was performed by a medical expert to generate reference data using “SliceOmatic.” The Dice similarity coefficient (DSC) was used to measure overlap between the results of the manual and the automated segmentations. The DSC value for each method was compared with the Mann–Whitney U test. Results A total of 676 hospitalized individuals was retrospectively included (365 males [53.8%] and 312 females [46.2%]). The median DSC for SliceOmatic vs AutoMATiCA (0.969 [5th percentile: 0.909]) was greater than the median DSC for SliceOmatic vs. ABACS‐SliceOmatic (0.949 [5th percentile: 0.836]) (p < 0.001). Conclusions AutoMATiCA, which used artificial intelligence, was more reliable than ABACS‐SliceOmatic for skeletal muscle segmentation at the L3 level in a cohort of hospitalized individuals. The next step is to develop and validate a neural network that can identify L3 slices, which is currently a fastidious process.