Efficient cascaded V‐net optimization for lower extremity CT segmentation validated using bone morphology assessment
| العنوان: | Efficient cascaded V‐net optimization for lower extremity CT segmentation validated using bone morphology assessment |
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| المؤلفون: | Ruurd J. A. Kuiper, Ralph J. B. Sakkers, Marijn van Stralen, Vahid Arbabi, Max A. Viergever, Harrie Weinans, Peter R. Seevinck |
| المصدر: | Journal of Orthopaedic Research. 40:2894-2907 |
| بيانات النشر: | Wiley, 2022. |
| سنة النشر: | 2022 |
| مصطلحات موضوعية: | Lower Extremity, Image Processing, Computer-Assisted, Humans, Orthopedics and Sports Medicine, Tomography, X-Ray Computed, Bone and Bones |
| الوصف: | Semantic segmentation of bone from lower extremity computerized tomography (CT) scans can improve and accelerate the visualization, diagnosis, and surgical planning in orthopaedics. However, the large field of view of these scans makes automatic segmentation using deep learning based methods challenging, slow and graphical processing unit (GPU) memory intensive. We investigated methods to more efficiently represent anatomical context for accurate and fast segmentation and compared these with state-of-the-art methodology. Six lower extremity bones from patients of two different datasets were manually segmented from CT scans, and used to train and optimize a cascaded deep learning approach. We varied the number of resolution levels, receptive fields, patch sizes, and number of V-net blocks. The best performing network used a multi-stage, cascaded V-net approach with 128 |
| تدمد: | 1554-527X 0736-0266 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ccad9d65f7fff59a0af7e92e6197d2ec https://doi.org/10.1002/jor.25314 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....ccad9d65f7fff59a0af7e92e6197d2ec |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 1554527X 07360266 |
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