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Syst."],"published-print":{"date-parts":[[2023,6]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>We aim to develop a deep-learning-based method for automatic proximal femur segmentation in quantitative computed tomography (QCT) images. We proposed a spatial transformation V-Net (ST-V-Net), which contains a V-Net and a spatial transform network (STN) to extract the proximal femur from QCT images. The STN incorporates a shape prior into the segmentation network as a constraint and guidance for model training, which improves model performance and accelerates model convergence. Meanwhile, a multi-stage training strategy is adopted to fine-tune the weights of the ST-V-Net. We performed experiments using a QCT dataset which included 397 QCT subjects. During the experiments for the entire cohort and then for male and female subjects separately, 90% of the subjects were used in ten-fold stratified cross-validation for training and the rest of the subjects were used to evaluate the performance of models. In the entire cohort, the proposed model achieved a Dice similarity coefficient (DSC) of 0.9888, a sensitivity of 0.9966 and a specificity of 0.9988. Compared with V-Net, the Hausdorff distance was reduced from 9.144 to 5.917\u00a0mm, and the average surface distance was reduced from 0.012 to 0.009\u00a0mm using the proposed ST-V-Net. Quantitative evaluation demonstrated excellent performance of the proposed ST-V-Net for automatic proximal femur segmentation in QCT images. In addition, the proposed ST-V-Net sheds light on incorporating shape prior to segmentation to further improve the model performance.<\/jats:p>","DOI":"10.1007\/s40747-021-00427-5","type":"journal-article","created":{"date-parts":[[2021,6,16]],"date-time":"2021-06-16T05:03:06Z","timestamp":1623819786000},"page":"2747-2758","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["ST-V-Net: incorporating shape prior into convolutional neural networks for proximal femur segmentation"],"prefix":"10.1007","volume":"9","author":[{"given":"Chen","family":"Zhao","sequence":"first","affiliation":[]},{"given":"Joyce H.","family":"Keyak","sequence":"additional","affiliation":[]},{"given":"Jinshan","family":"Tang","sequence":"additional","affiliation":[]},{"given":"Tadashi S.","family":"Kaneko","sequence":"additional","affiliation":[]},{"given":"Sundeep","family":"Khosla","sequence":"additional","affiliation":[]},{"given":"Shreyasee","family":"Amin","sequence":"additional","affiliation":[]},{"given":"Elizabeth J.","family":"Atkinson","sequence":"additional","affiliation":[]},{"given":"Lan-Juan","family":"Zhao","sequence":"additional","affiliation":[]},{"given":"Michael J.","family":"Serou","sequence":"additional","affiliation":[]},{"given":"Chaoyang","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Hui","family":"Shen","sequence":"additional","affiliation":[]},{"given":"Hong-Wen","family":"Deng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6039-959X","authenticated-orcid":false,"given":"Weihua","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,16]]},"reference":[{"key":"427_CR1","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2019\/6743489","volume":"2019","author":"J Liu","year":"2019","unstructured":"Liu J, Curtis E, Cooper C, Harvey NC (2019) State of the art in osteoporosis risk assessment and treatment. 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