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Title: | A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment | Authors: | Shen, H He, P Ren, Y Huang, Z Li, S Wang, G Cong, M Luo, D Shao, D Lee, EYP Cui, R Huo, L Qin, J Liu, J Hu, Z Liu, Z |
Issue Date: | 1-Mar-2023 | Source: | Quantitative imaging in medicine and surgery, 1 Mar. 2023, v. 13, no. 3, p. 1384-1398 | Abstract: | Background: Quantitative muscle and fat data obtained through body composition analysis are expected to be a new stable biomarker for the early and accurate prediction of treatment-related toxicity, treatment response, and prognosis in patients with lung cancer. The use of these biomarkers can enable the adjustment of individualized treatment regimens in a timely manner, which is critical to further improving patient prognosis and quality of life. We aimed to develop a deep learning model based on attention for fully automated segmentation of the abdomen from computed tomography (CT) to quantify body composition. Methods: A fully automatic segmentation deep learning model was designed based on the attention mechanism and using U-Net as the framework. Subcutaneous fat, skeletal muscle, and visceral fat were manually segmented by two experts to serve as ground truth labels. The performance of the model was evaluated using Dice similarity coefficients (DSCs) and Hausdorff distance at 95th percentile (HD95). Results: The mean DSC for subcutaneous fat and skeletal muscle were high for both the enhanced CT test set (0.93±0.06 and 0.96±0.02, respectively) and the plain CT test set (0.90±0.09 and 0.95±0.01, respectively). Nevertheless, the model did not perform well in the segmentation performance of visceral fat, especially for the enhanced CT test set. The mean DSC for the enhanced CT test set was 0.87±0.11, while the mean DSC for the plain CT test set was 0.92±0.03. We discuss the reasons for this result. Conclusions: This work demonstrates a method for the automatic outlining of subcutaneous fat, skeletal muscle, and visceral fat areas at L3. |
Keywords: | CT scan Deep learning Fat segmentation Muscle segmentation Sarcopenia |
Publisher: | AME Publishing Company | Journal: | Quantitative imaging in medicine and surgery | ISSN: | 2223-4292 | EISSN: | 2223-4306 | DOI: | 10.21037/qims-22-330 | Rights: | © Quantitative Imaging in Medicine and Surgery. All rights reserved. This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/. The following publication Shen H, He P, Ren Y, Huang Z, Li S, Wang G, Cong M, Luo D, Shao D, Lee EYP, Cui R, Huo L, Qin J, Liu J, Hu Z, Liu Z, Zhang N. A deep learning model based on the attention mechanism for automatic segmentation of abdominal muscle and fat for body composition assessment. Quant Imaging Med Surg 2023;13(3):1384-1398 is available at https://doi.org/10.21037/qims-22-330. |
Appears in Collections: | Journal/Magazine Article |
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