{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:22:21Z","timestamp":1771467741559,"version":"3.50.1"},"reference-count":25,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T00:00:00Z","timestamp":1655424000000},"content-version":"vor","delay-in-days":167,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["BioMed Research International"],"published-print":{"date-parts":[[2022,1]]},"abstract":"<jats:p><jats:italic>Background<\/jats:italic>. Idiopathic scoliosis accounts for over 80% of all cases of scoliosis but has an unclear pathogenic mechanism. Many studies have introduced conventional image processing methods, but the results often fail to meet expectations. With the improvement and evolution of research in neural networks in the field of deep learning, many research efforts related to spinal reconstruction using the convolutional neural network (CNN) architecture of deep learning have shown promise. <jats:italic>Purpose<\/jats:italic>. To investigate the use of CNN for spine modeling. <jats:italic>Methods<\/jats:italic>. The primary technique used in this study involves Mask Region\u2010based CNN (R\u2010CNN) image segmentation and object detection methods as applied to spine model positioning of radiographs. The methods were evaluated based on common evaluation criteria for vertebral segmentation and object detection. Evaluations were performed using the loss function, mask loss function, classification loss function, target box loss function, average accuracy, and average recall. <jats:italic>Results<\/jats:italic>. Many bony structures were directly identified in one step, including the lumbar spine (L1\u2010L5) and thoracic spine (T1\u2010T12) in frontal and lateral radiographs, thereby achieving initial positioning of the statistical spine model to provide spine model positioning for future reconstruction and classification prediction. An average detection box accuracy of 97.4% and an average segmentation accuracy of 96.8% were achieved for the prediction efficacy of frontal images, with good image visualization. Moreover, the results for lateral images were satisfactory considering the evaluation parameters and image visualization. <jats:italic>Conclusion<\/jats:italic>. Mask R\u2010CNN can be used for effective positioning in spine model studies for future reconstruction and classification prediction.<\/jats:p>","DOI":"10.1155\/2022\/7512445","type":"journal-article","created":{"date-parts":[[2022,6,17]],"date-time":"2022-06-17T20:50:06Z","timestamp":1655499006000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Region\u2010Based Convolutional Neural Network\u2010Based Spine Model Positioning of X\u2010Ray Images"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7540-9562","authenticated-orcid":false,"given":"Le","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiabao","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2743-3466","authenticated-orcid":false,"given":"Song","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2022,6,17]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1001\/jama.2017.19342"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00381-018-3915-6"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1097\/BPO.0000000000000922"},{"key":"e_1_2_8_4_2","doi-asserted-by":"publisher","DOI":"10.1097\/BPO.0b013e31802b4993"},{"key":"e_1_2_8_5_2","doi-asserted-by":"crossref","unstructured":"KumarS. NayakK. P. andHareeshaK. S. Combined SCP and geometric reconstruction of spine from biplanar radiographs 437 Proceedings of Fifth International Conference on Soft Computing for Problem Solving 2016 Singapore 215\u2013225 Advances in Intelligent Systems and Computing https:\/\/doi.org\/10.1007\/978-981-10-0451-3_21 2-s2.0-84964865128.","DOI":"10.1007\/978-981-10-0451-3_21"},{"key":"e_1_2_8_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2018.01.006"},{"key":"e_1_2_8_7_2","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2014.00058"},{"key":"e_1_2_8_8_2","doi-asserted-by":"crossref","unstructured":"CressonT. GodboutB. BranchaudD. ChavR. GravelP. andDe GuiseJ. A. Surface reconstruction from planar X-ray images using moving least squares 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2008 Vancouver BC Canada 3967\u20133970 https:\/\/doi.org\/10.1109\/IEMBS.2008.4650078.","DOI":"10.1109\/IEMBS.2008.4650078"},{"key":"e_1_2_8_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2007.905081"},{"key":"e_1_2_8_10_2","doi-asserted-by":"publisher","DOI":"10.4236\/am.2016.72013"},{"key":"e_1_2_8_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00586-002-0432-8"},{"key":"e_1_2_8_12_2","doi-asserted-by":"crossref","unstructured":"BoisvertJ.andMouraD. C. Interactive 3D reconstruction of the spine from radiographs using a statistical shape model and second-order cone programming 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society 2011 Boston MA USA 5726\u20135729 https:\/\/doi.org\/10.1109\/IEMBS.2011.6091386 2-s2.0-84862290631.","DOI":"10.1109\/IEMBS.2011.6091386"},{"key":"e_1_2_8_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2011.03.007"},{"key":"e_1_2_8_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2009.01.003"},{"key":"e_1_2_8_15_2","doi-asserted-by":"publisher","DOI":"10.1097\/BRS.0b013e3182293548"},{"key":"e_1_2_8_16_2","doi-asserted-by":"publisher","DOI":"10.1117\/12.2293763"},{"key":"e_1_2_8_17_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2019.2914400"},{"key":"e_1_2_8_18_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-019-01247-4"},{"key":"e_1_2_8_19_2","doi-asserted-by":"crossref","unstructured":"HeK. XiangyuZ. ShaoqingR. andSunJ. Deep residual learning for image recognition Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2016 Las Vegas 770\u2013778 https:\/\/doi.org\/10.1109\/CVPR.2016.90 2-s2.0-84986274465.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_2_8_20_2","doi-asserted-by":"crossref","unstructured":"SzegedyC. IoffeS. VanhouckeV. andAlemiA. A. Inception-v4 Inception-ResNet and the impact of residual connections on learning Thirty-first AAAI Conference on Artificial Intelligence 2017 San Francisco California USA 4278\u20134284 https:\/\/doi.org\/10.48550\/arXiv.1602.07261.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"e_1_2_8_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2019.01.006"},{"key":"e_1_2_8_22_2","doi-asserted-by":"crossref","unstructured":"LinT. Y. Doll\u00e1rP. GirshickR. HeK. HariharanB. andBelongieS. Feature pyramid networks for object detection Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition 2017 Honolulu Hawaii USA 2117\u20132125 https:\/\/doi.org\/10.48550\/arXiv.1612.03144.","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_2_8_23_2","doi-asserted-by":"crossref","unstructured":"LiangZ. ShaoJ. ZhangD. andGaoL. Small object detection using deep feature pyramid networks Pacific Rim Conference on Multimedia 2018 Hefei China 554\u2013564 https:\/\/doi.org\/10.1007\/978-3-030-00764-5_51 2-s2.0-85054497417.","DOI":"10.1007\/978-3-030-00764-5_51"},{"key":"e_1_2_8_24_2","doi-asserted-by":"crossref","unstructured":"KirillovA. GirshickR. HeK. andDoll\u00e1rP. Panoptic feature pyramid networks Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition 2019 California USA 6399\u20136408 https:\/\/doi.org\/10.1109\/CVPR.2019.00656.","DOI":"10.1109\/CVPR.2019.00656"},{"key":"e_1_2_8_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2844175"}],"container-title":["BioMed Research International"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/7512445.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/bmri\/2022\/7512445.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2022\/7512445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:09:37Z","timestamp":1735690177000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2022\/7512445"}},"subtitle":[],"editor":[{"given":"Chen","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,1]]},"references-count":25,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["10.1155\/2022\/7512445"],"URL":"https:\/\/doi.org\/10.1155\/2022\/7512445","archive":["Portico"],"relation":{},"ISSN":["2314-6133","2314-6141"],"issn-type":[{"value":"2314-6133","type":"print"},{"value":"2314-6141","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1]]},"assertion":[{"value":"2022-04-23","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-05-31","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-06-17","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"7512445"}}