ArticleComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2021
Fully automated 3D segmentation of MR-imaged calf muscle compartments: Neighborhood relationship enhanced fully convolutional network.
Article in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- MRI image segmentation of the major lower leg muscles using deep learning: application in biomechanical analysis.Medical & biological engineering & computing · 2026Article
- Imaging in the diagnosis and characterization of tennis leg.Skeletal radiology · 2026Review
- Deep learning-based framework for comprehensive quantification of thigh and calf muscles and adipose tissues from MRI.Magma (New York, N.Y.) · 2026Article
- Optimizing MRI annotation workflows for high-accuracy deep learning thigh muscle segmentation in athletes.Radiology advances · 2026Article
- Efficient musculoskeletal annotation using free-form deformation.Scientific reports · 2024Article
- Article
- Assisted annotation in Deep LOGISMOS: Simultaneous multi-compartment 3D MRI segmentation of calf muscles.Medical physics · 2023Article
- Hexagonal-Grid-Layout Image Segmentation Using Shock Filters: Computational Complexity Case Study for Microarray Image Analysis Related to Machine Learning Approaches.Sensors (Basel, Switzerland) · 2023Article
- KCB-Net: A 3D knee cartilage and bone segmentation network via sparse annotation.Medical image analysis · 2022Article
- CMC-Net: 3D calf muscle compartment segmentation with sparse annotation.Medical image analysis · 2022Article
- 3D Automated Segmentation of Lower Leg Muscles Using Machine Learning on a Heterogeneous Dataset.Diagnostics (Basel, Switzerland) · 2021Article
- High Inter-Rater Reliability of Manual Segmentation and Volume-Based Tractography in Healthy and Dystrophic Human Calf Muscle.Diagnostics (Basel, Switzerland) · 2021Article
- Evaluation of Amyotrophic Lateral Sclerosis-Induced Muscle Degeneration Using Magnetic Resonance-Based Relaxivity Contrast Imaging (RCI).Tomography (Ann Arbor, Mich.) · 2021Article
- Quantitative muscle MRI as a sensitive marker of early muscle pathology in myotonic dystrophy type 1.Muscle & nerve · 2021Article
- Overview of MR Image Segmentation Strategies in Neuromuscular Disorders.Frontiers in neurology · 2021Review
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8 authors.
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Abstract
Automated segmentation of individual calf muscle compartments from 3D magnetic resonance (MR) images is essential for developing quantitative biomarkers for muscular disease progression and its prediction. Achieving clinically acceptable results is a challenging task due to large variations in muscle shape and MR appearance. In this paper, we present a novel fully convolutional network (FCN) that utilizes contextual information in a large neighborhood and embeds edge-aware constraints for individual calf muscle compartment segmentations. An encoder-decoder architecture is used to systematically enlarge convolution receptive field and preserve information at all resolutions. Edge positions derived from the FCN output muscle probability maps are explicitly regularized using kernel-based edge detection in an end-to-end optimization framework. Our method was evaluated on 40 T1-weighted MR images of 10 healthy and 30 diseased subjects by fourfold cross-validation. Mean DICE coefficients of 88.00-91.29% and mean absolute surface positioning errors of 1.04-1.66 mm were achieved for the five 3D muscle compartments.
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