Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images.
Automated segmentation of skeletal muscle from computed tomography (CT) images is essential for large-scale quantitative body composition analysis. However, manual segmentation is time-consuming and impractical for routine or high-throughput use. This study presents a systematic comparison of two-di...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 39; no. 3; pp. 2110 - 2123 |
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| Main Authors: | , , , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Jun2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=194225532&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 194225532 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Jun2026 vid: 39 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 194225532 189894346 194225532 194225532 10.1007/s10278-025-01646-9 194225532 ppf: 2110 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning-Based 3D and 2D Approaches for Skeletal Muscle Segmentation on Low-Dose CT Images. aug: au: Timpano, Giuseppe Veltri, Pierangelo Vizza, Patrizia Cascini, Giuseppe Lucio Manti, Francesco affil: https://ror.org/0530bdk91 Department of Surgical and Medical Sciences, Magna Graecia University, 88100, Catanzaro, Italy sug: subj: Muscle, Skeletal Radiography Lumbar Vertebrae Radiography Tomography, X-Ray Computed Image Processing, Computer Assisted Radiation Dosage Imaging, Three-Dimensional Deep Learning Evaluation Sensitivity and Specificity Human Comparative Studies Paired T-Tests Effect Size Descriptive Statistics Data Analysis Software Computer Simulation Algorithms ab: Automated segmentation of skeletal muscle from computed tomography (CT) images is essential for large-scale quantitative body composition analysis. However, manual segmentation is time-consuming and impractical for routine or high-throughput use. This study presents a systematic comparison of two-dimensional (2D) and three-dimensional (3D) deep learning architectures for segmenting skeletal muscle at the anatomically standardized level of the third lumbar vertebra (L3) in low-dose computed tomography (LDCT) scans. We implemented and evaluated the DeepLabv3+ (2D) and UNet3+ (3D) architectures on a curated dataset of 537 LDCT scans, applying preprocessing protocols, L3 slice selection, and region of interest extraction. The model performance was evaluated using a comprehensive set of evaluation metrics, including Dice similarity coefficient (DSC) and 95th percentile Hausdorff distance (HD95). DeepLabv3+ achieved the highest segmentation accuracy (DSC = 0.982 ± 0.010, HD95 = 1.04 ± 0.46 mm), while UNet3+ showed competitive performance (DSC = 0.967 ± 0.013, HD95 = 1.27 ± 0.58 mm) with 26 times fewer parameters (1.27 million vs. 33.6 million) and lower inference time. Both models exceeded or matched results reported in the recent CT-based muscle segmentation literature. This work offers practical insights into architecture selection for automated LDCT-based muscle segmentation workflows, with a focus on the L3 vertebral level, which remains the gold standard in muscle quantification protocols. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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