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Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>To propose a deep learning framework \u201cSpineCurve-net\u201d for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients.\u00a0A total of 116 scoliosis patients were analyzed, divided into a training set of 89 patients (average age 32.4\u2009\u00b1\u200924.5 years) and a validation set of 27 patients (average age 17.3\u2009\u00b1\u20095.8 years). Vertebral identification and curve fitting were achieved through U-net and NURBS-net and resulted in a Non-Uniform Rational B-Spline (NURBS) curve of the spine. The 3D Cobb angles were measured in two ways: the predicted 3D Cobb angle (PRED-3D-CA), which is the maximum value in the smoothed angle map derived from the NURBS curve, and the 2D mapping Cobb angle (MAP-2D-CA), which is the maximal angle formed by the tangent vectors along the projected 2D spinal curve.\u00a0The model segmented spinal masks effectively, capturing easily missed vertebral bodies. Spoke kernel filtering distinguished vertebral regions, centralizing spinal curves. The SpineCurve Network method\u2019s Cobb angle (PRED-3D-CA and MAP-2D-CA) measurements correlated strongly with the surgeons\u2019 annotated Cobb angle (ground truth, GT) based on 2D radiographs, revealing high Pearson correlation coefficients of 0.983 and 0.934, respectively.\u00a0This paper proposed an automated technique for calculating the 3D Cobb angle in preoperative scoliosis patients, yielding results that are highly correlated with traditional 2D Cobb angle measurements. Given its capacity to accurately represent the three-dimensional nature of spinal deformities, this method shows potential in aiding physicians to develop more precise surgical strategies in upcoming cases.<\/jats:p>","DOI":"10.1007\/s10278-024-01211-w","type":"journal-article","created":{"date-parts":[[2024,8,8]],"date-time":"2024-08-08T18:07:20Z","timestamp":1723140440000},"page":"309-317","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients"],"prefix":"10.1007","volume":"38","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2506-1796","authenticated-orcid":false,"given":"Lening","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Teng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fan","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Man-Sang","family":"Wong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,8,8]]},"reference":[{"issue":"4","key":"1211_CR1","doi-asserted-by":"publisher","first-page":"E2","DOI":"10.3171\/2017.7.FOCUS17297","volume":"43","author":"A Jada","year":"2017","unstructured":"Jada, A., C.E. 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The local ethics committee approved the experimental protocol.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Standards"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}]}}