A comparative study of two automated solutions for cross-sectional skeletal muscle measurement from abdominal computed tomography images.

Autor: Charrière K; Public Health Department, Clinical Investigation Center-Technological, Innovation, INSERM CIC1406, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France., Boulouard Q; Public Health Department, Clinical Investigation Center-Technological, Innovation, INSERM CIC1406, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France., Artemova S; Public Health Department, Clinical Investigation Center-Technological, Innovation, INSERM CIC1406, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France., Vilotitch A; CHU Grenoble Alpes, Cellule d'ingénierie des données, Grenoble, France., Ferretti GR; INSERM U1209, IAB, CHU Grenoble Alpes, Service de radiologie diagnostique et interventionnelle, Université Grenoble Alpes, Grenoble, France., Bosson JL; Public Health Department, Clinical Investigation Center-Technological, Innovation, INSERM CIC1406, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France.; CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, Public Health Department, TIMC, Université Grenoble Alpes, Grenoble, France., Moreau-Gaudry A; Public Health Department, Clinical Investigation Center-Technological, Innovation, INSERM CIC1406, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France.; CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, Public Health Department, TIMC, Université Grenoble Alpes, Grenoble, France., Giai J; Public Health Department, Clinical Investigation Center-Technological, Innovation, INSERM CIC1406, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France.; CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, Public Health Department, TIMC, Université Grenoble Alpes, Grenoble, France., Fontaine E; Department of Endocrinology, Diabetology and Nutrition, INSERM U1055, LBFA, CHU Grenoble Alpes, Université Grenoble Alpes, Grenoble, France., Bétry C; Department of Endocrinology, Diabetology and Nutrition, CNRS, UMR 5525, VetAgro Sup, Grenoble INP, CHU Grenoble Alpes, TIMC, Université Grenoble Alpes, Grenoble, France.
Jazyk: angličtina
Zdroj: Medical physics [Med Phys] 2023 Aug; Vol. 50 (8), pp. 4973-4980. Date of Electronic Publication: 2023 Feb 16.
DOI: 10.1002/mp.16261
Abstrakt: 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.
(© 2023 American Association of Physicists in Medicine.)
Databáze: MEDLINE
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