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Recently, many deep learning approaches have been proposed to solve the problem of manual segmentation, which is time-consuming and operator-dependent. In the present study, SegResNet has been adapted from other domains, such as brain tumors, for knee joints, in particular, to segment the femoral bone from magnetic resonance images. This algorithm has been compared to the well-known U-Net in terms of evaluation metrics, such as the Dice similarity coefficient and Hausdorff distance. In the training phase, various combinations of hyperparameters, such as epochs and learning rates, have been tested to determine which combination produced the most accurate results. Based on their comparable results, both U-Net and SegResNet performed well in accurately segmenting the femur. Dice similarity coefficients of 0.94 and Hausdorff distances less than or equal to 1 mm indicate that both models are effective at capturing anatomical boundaries in the femur. According to the results of this study, SegResNet is a viable option for automating the creation of 3D femur models. In the future, the performance and applicability of SegResNet in real-world settings will be further validated and tested using a variety of datasets and clinical scenarios.<\/jats:p>","DOI":"10.1115\/1.4064450","type":"journal-article","created":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T14:01:03Z","timestamp":1704722463000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":10,"title":["Comparative Analysis of Convolutional Neural Network Architectures for Automated Knee Segmentation in Medical Imaging: A Performance Evaluation"],"prefix":"10.1115","volume":"24","author":[{"given":"Anna","family":"Ghidotti","sequence":"first","affiliation":[{"name":"University of Bergamo Department of Management, Information and Production Engineering, , Via Pasubio 7b, Dalmine, Bergamo 24044 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrea","family":"Vitali","sequence":"additional","affiliation":[{"name":"University of Bergamo Department of Management, Information and Production Engineering, , Via Pasubio 7b, Dalmine, Bergamo 24044 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniele","family":"Regazzoni","sequence":"additional","affiliation":[{"name":"University of Bergamo Department of Management, Information and Production Engineering, , Via Pasubio 7b, Dalmine, Bergamo 24044 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miri Weiss","family":"Cohen","sequence":"additional","affiliation":[{"name":"Braude College of Engineering Department of Software Engineering, , Snunit St., Karmiel 185200 , Israel"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Caterina","family":"Rizzi","sequence":"additional","affiliation":[{"name":"University of Bergamo Department of Management, Information and Production Engineering, , Via Pasubio 7b, Dalmine, Bergamo 24044 , Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"33","published-online":{"date-parts":[[2024,1,29]]},"reference":[{"key":"2024041814391807300_CIT0001","first-page":"574","article-title":"UNETR: Transformers for 3D Medical Image Segmentation","author":"Hatamizadeh","year":"2022"},{"key":"2024041814391807300_CIT0002","first-page":"5039","article-title":"CEL-Unet: A Novel CNN Architecture for 3D Segmentation of Knee Bones Affected by Severe Osteoarthritis for PSI-Based Surgical Planning","author":"Marsilio","year":"2022"},{"issue":"6","key":"2024041814391807300_CIT0003","doi-asserted-by":"publisher","first-page":"061006","DOI":"10.1115\/1.4055427\/1145938","article-title":"Evaluation of Clinical and Technical Parameters to Customize Total Knee Arthroplasty Implants","volume":"22","author":"Ghidotti","year":"2022","journal-title":"ASME J. 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