{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T21:18:19Z","timestamp":1788297499321,"version":"build-2803163510"},"reference-count":25,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T00:00:00Z","timestamp":1679443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Chair in Medical Imaging and Artificial Intelligence, a joint Hospital-University Chair between the University of Toronto, The Hospital for Sick Children, and the SickKids Foundation"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Can Assoc Radiol J"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:p>\n                    <jats:bold>Purpose:<\/jats:bold>\n                    Scoliosis is a deformity of the spine, and as a measure of scoliosis severity, Cobb angle is fundamental to the diagnosis of deformities that require treatment. Conventional Cobb angle measurement and assessment is usually done manually, which is inherently time-consuming, and associated with high inter- and intra-observer variability. While there exist automatic scoliosis measurement methods, they suffer from insufficient accuracy. In this work, we propose a two-step segmentation-based deep learning architecture to automate Cobb angle measurement for scoliosis assessment using X-Ray images.\n                    <jats:bold>Methods:<\/jats:bold>\n                    The proposed architecture involves two steps. In the first step, we utilize a novel Augmented U-Net architecture to generate segmentations of vertebrae. The second step includes a non-learning-based pipeline to extract landmark coordinates from the segmented vertebrae and filter undesirable landmarks.\n                    <jats:bold>Results:<\/jats:bold>\n                    Our proposed Augmented U-Net architecture achieved a Symmetric Mean Absolute Percentage Error of 9.2%, with approximately 90% of estimations having less than 10 degrees difference compared with the AASCE-MICCAI challenge 2019 dataset ground truths. We further validated the model using an internal dataset and achieved almost the same level of performance.\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    The proposed architecture is robust in providing automated spinal vertebrae segmentations and Cobb angle measurement, and is potentially generalizable to real-world clinical settings.\n                  <\/jats:p>","DOI":"10.1177\/08465371231163187","type":"journal-article","created":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:17:17Z","timestamp":1679530637000},"page":"667-675","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Automated Adolescence Scoliosis Detection Using Augmented U-Net With Non-square Kernels"],"prefix":"10.1177","volume":"74","author":[{"given":"Yujie","family":"Wu","sequence":"first","affiliation":[{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0497-6206","authenticated-orcid":false,"given":"Khashayar","family":"Namdar","sequence":"additional","affiliation":[{"name":"University of Toronto"},{"name":"The Hospital for Sick Children"},{"name":"Vector Institute"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chaojun","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahob","family":"Hosseinpour","sequence":"additional","affiliation":[{"name":"The Hospital for Sick Children"},{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manohar","family":"Shroff","sequence":"additional","affiliation":[{"name":"The Hospital for Sick Children"},{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea S.","family":"Doria","sequence":"additional","affiliation":[{"name":"University of Toronto"},{"name":"The Hospital for Sick Children"},{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5616-8660","authenticated-orcid":false,"given":"Farzad","family":"Khalvati","sequence":"additional","affiliation":[{"name":"University of Toronto"},{"name":"University of Toronto"},{"name":"The Hospital for Sick Children"},{"name":"Vector Institute"},{"name":"University of Toronto"},{"name":"University of Toronto"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2023,3,22]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1093\/pch\/12.9.771"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00586-013-2693-9"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.spinee.2010.02.020"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00586-007-0401-3"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","unstructured":"Wu H Bailey C Rasoulinejad P Li S. 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