ArticleJournal of medical systems2019
Automated Fractured Bone Segmentation and Labeling from CT Images.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed.
- A new scheme for knee joint CT image segmentation: A max-flow and watershed method driving semi-automatic CT knee bone segmentation.Scientific reports · 2026Article
- Automated segmentation of the fibula from CT imaging using two-stepped deep learning in 3D U-Net architectures.Scientific reports · 2025Article
- An Overview of Artificial Intelligence Applications in Radiological Imaging for Bone Fracture Diagnosis.Cureus · 2025Review
- An End-to-End Geometry-Based Pipeline for Automatic Preoperative Surgical Planning of Pelvic Fracture Reduction and Fixation.IEEE transactions on medical imaging · 2025Article
- Exploring Fracture Patterns: Assessing Representation Methods for Bone Fracture Simulation.Journal of personalized medicine · 2024Article
- Application of Finite Element Analysis Combined With Virtual Computer in Preoperative Planning of Distal Femoral Fracture.Frontiers in surgery · 2022Article
- 5KJournal of medical systems · 2021Article
- Fracture reduction planning and guidance in orthopaedic trauma surgery via multi-body image registration.Medical image analysis · 2021Article
- Autonomous image segmentation and identification of anatomical landmarks from lumbar spine intraoperative computed tomography scans using machine learning: A validation study.Journal of craniovertebral junction & spineArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Within the scope of education and training, automatic and accurate segmentation of fractured bones from Computed Tomographic (CT) images is the fundamental step in several different applications, such as trauma analysis, visualization, diagnosis, surgical planning and simulation. It helps physicians analyze the severity of injury by taking into account the following fracture features, such as location of the fracture, number of pieces and deviation from the original location. Besides, it helps provide accurate 3D visualization and decide optimal recovery plans/processes. To accurately segment fracture bones from CT images, in the paper, we introduce a segmentation technique that makes labeling process easier. Based on the patient-specific anatomy, unique labels are assigned. Unlike conventional techniques, it also includes the removal of unwanted artifacts, such as flesh. In our experiments, we have demonstrated our concept with real-world data (with an accuracy of 95.45%) and have compared with state-of-the-art techniques. For validation, our tests followed expert-based decisions i.e., clinical ground-truth. With the results, our collection of 8000 CT images will be available upon the request.
Indexed as
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.