ArticleInternational journal of computer assisted radiology and surgery2024
Two-stage multi-task deep learning framework for simultaneous pelvic bone segmentation and landmark detection from CT images.
Article in International journal of computer assisted radiology and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Segmentation methods for quantifying X-ray Computed Tomography based biomarkers to assess hip fracture risk: a systematic literature review.Frontiers in bioengineering and biotechnology · 2024Pooled it
- Artificial Intelligence Applications in Implant Positioning, Dislocation Risk Prediction, and Surgical Indications in Orthopaedic Surgery.Bioengineering (Basel, Switzerland) · 2026Review
- Dynamic wavelet transform normalized multi scale dense attention UNet with Binary Horse Herd Optimization for osteosarcoma diagnosis.Scientific reports · 2026Article
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
Corrections and comments
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Authors and funding
14 authors.
Funding
Abstract
purposePelvic bone segmentation and landmark definition from computed tomography (CT) images are prerequisite steps for the preoperative planning of total hip arthroplasty. In clinical applications, the diseased pelvic anatomy usually degrades the accuracies of bone segmentation and landmark detection, leading to improper surgery planning and potential operative complications.
methodsThis work proposes a two-stage multi-task algorithm to improve the accuracy of pelvic bone segmentation and landmark detection, especially for the diseased cases. The two-stage framework uses a coarse-to-fine strategy which first conducts global-scale bone segmentation and landmark detection and then focuses on the important local region to further refine the accuracy. For the global stage, a dual-task network is designed to share the common features between the segmentation and detection tasks, so that the two tasks mutually reinforce each other's performance. For the local-scale segmentation, an edge-enhanced dual-task network is designed for simultaneous bone segmentation and edge detection, leading to the more accurate delineation of the acetabulum boundary.
resultsThis method was evaluated via threefold cross-validation based on 81 CT images (including 31 diseased and 50 healthy cases). The first stage achieved DSC scores of 0.94, 0.97, and 0.97 for the sacrum, left and right hips, respectively, and an average distance error of 3.24 mm for the bone landmarks. The second stage further improved the DSC of the acetabulum by 5.42%, and this accuracy outperforms the state-of-the-arts (SOTA) methods by 0.63%. Our method also accurately segmented the diseased acetabulum boundaries. The entire workflow took ~ 10 s, which was only half of the U-Net run time.
conclusionUsing the multi-task networks and the coarse-to-fine strategy, this method achieved more accurate bone segmentation and landmark detection than the SOTA method, especially for diseased hip images. Our work contributes to accurate and rapid design of acetabular cup prostheses.
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