ArticleInternational journal of computer assisted radiology and surgery2018
Automated muscle segmentation from CT images of the hip and thigh using a hierarchical multi-atlas method.
Article in International journal of computer assisted radiology and surgery, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 37 citations in OpenAlex.
- A two-stage deep-learning model using MobileNetV2 and U-Net for CT-based muscle volume assessment in total hip arthroplasty.Scientific reports · 2026Article
- Article
- Recent topics in musculoskeletal imaging focused on clinical applications of AI: How should radiologists approach and use AI?La Radiologia medica · 2025Review
- Validation of musculoskeletal segmentation model with uncertainty estimation for bone and muscle assessment in hip-to-knee clinical CT images.Scientific reports · 2025Article
- Article
- Single slice thigh CT muscle group segmentation with domain adaptation and self-training.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Article
- Utility of a novel integrated deep convolutional neural network for the segmentation of hip joint from computed tomography images in the preoperative planning of total hip arthroplasty.Journal of orthopaedic surgery and research · 2022Article
- Three-Dimensional Segmentation andFrontiers in veterinary science · 2021Article
- Perspective for Future Medicine: Multidisciplinary Computational Anatomy-Based Medicine with Artificial Intelligence.Cyborg and bionic systems (Washington, D.C.) · 2021Article
- Deep generative models for automated muscle segmentation in computed tomography scanning.PloS one · 2021Article
- A Knowledge-Based Modality-Independent Technique for Concurrent Thigh Muscle Segmentation: Applicable to CT and MR Images.Journal of digital imaging · 2020Article
- Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.Academic radiology · 2019Article
- Quantitative analysis of skeletal muscle by computed tomography imaging-State of the art.Journal of orthopaedic translation · 2018Review
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
Abstract
purposePatient-specific quantitative assessments of muscle mass and biomechanical musculoskeletal simulations require segmentation of the muscles from medical images. The objective of this work is to automate muscle segmentation from CT data of the hip and thigh.
methodWe propose a hierarchical multi-atlas method in which each hierarchy includes spatial normalization using simpler pre-segmented structures in order to reduce the inter-patient variability of more complex target structures.
resultsThe proposed hierarchical method was evaluated with 19 muscles from 20 CT images of the hip and thigh using the manual segmentation by expert orthopedic surgeons as ground truth. The average symmetric surface distance was significantly reduced in the proposed method (1.53 mm) in comparison with the conventional method (2.65 mm).
conclusionWe demonstrated that the proposed hierarchical multi-atlas method improved the accuracy of muscle segmentation from CT images, in which large inter-patient variability and insufficient contrast were involved.
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