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The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.

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Bibliographic Details
Title: The effect of deep learning-based lesion segmentation on failure load calculations of metastatic femurs using finite element analysis.
Authors: Ataei A; Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands. Electronic address: Ali.Ataei@radboudumc.nl., Eggermont F; Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands., Verdonschot N; Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands; Laboratory for Biomechanical Engineering, University of Twente, Enschede, the Netherlands., Lessmann N; Diagnostic Image Analysis Group, Department of Medical Imaging, Radboud university medical center, Nijmegen, the Netherlands., Tanck E; Orthopaedic Research Lab, Radboud university medical center, P.O. Box 9101, 6500, HB, Nijmegen, the Netherlands.
Source: Bone [Bone] 2024 Feb; Vol. 179, pp. 116987. Date of Electronic Publication: 2023 Dec 05.
Publication Type: Journal Article
Journal Info: Publisher: Elsevier Science Country of Publication: United States NLM ID: 8504048 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-2763 (Electronic) Linking ISSN: 18732763 NLM ISO Abbreviation: Bone Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1873-2763
DOI:10.1016/j.bone.2023.116987