Abstract
PurposeA lightweight deep learning network SMA-Net was proposed to intelligently segment the skeletal muscle of the third lumbar (L3) level in patients with cervical cancer radiotherapy, and the segmentation performance of the network was evaluated.Methods and MaterialsA total of 160 eligible patients with cervical cancer admitted to the oncology department of our hospital from September 2021 to June 2024 were randomly divided into training set (N = 112), validation set (N = 16) and test set (N = 32) according to 7 : 1 : 2. The lightweight Mamba architecture is introduced into the UNet network, and the SAB and CAB attention mechanisms are introduced on the skip connection. The attention mechanism is used to suppress the irrelevant information in the image and highlight the important local features. The trained network is geometrically evaluated on the test set for segmentation performance, comparison of manual segmentation and predicted skeletal muscle area (SMA). Compare the parameters and computations of SMA-Net with existing networks.ResultsThe dice similarity coefficient of SMA-Net network for skeletal muscle segmentation was 89.16%, the sensitivity SEN was 88.21%, the positive predictive value PPV was 90.13% and the 95% Hausdorff distance was 5.30mm. Manual segmentation is basically close to the predicted SMA. Our proposed network for cervical cancer patients predicted sarcopenia with 87.5% accuracy, 92.31% precision, 80% recall, 85.72% F1-Score, and 0.871 AUC. The calculation amount of SMA-UNet network is 1.50 GFLOPS, and the parameter amount is 1.24 M. The radiologist's scores show that minor and no revision accounted for 93.75% on manual revision of skeletal muscle.ConclusionThe lightweight SMA-Net proposed in this study can accurately segment L3 skeletal muscle and quickly calculate its area, which basically meets the clinical application and is convenient for clinical deployment. It is helpful for clinicians to quickly diagnose sarcopenia in patients with cervical cancer, save medical resources, reduce the workload of physicians, and improve diagnostic efficiency.