{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T11:53:17Z","timestamp":1782820397087,"version":"3.54.5"},"reference-count":39,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T00:00:00Z","timestamp":1734998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2025,1]]},"abstract":"\n The prevalence of spinal tuberculosis (ST) is particularly high in underdeveloped regions with inadequate medical conditions. This not only leads to misdiagnosis and delays in treatment progress but also contributes to the continued transmission of tuberculosis bacteria, posing a risk to other individuals. Currently, CT imaging is extensively utilized in computer-aided diagnosis (CAD). The main features of ST on CT images include bone destruction, osteosclerosis, sequestration formation, and intervertebral disc damage. However, manual diagnosis by doctors may result in subjective judgments and misdiagnosis. Therefore, an accurate and objective method is needed for diagnosing of spinal tuberculosis. In this paper, we put forward an assistive diagnostic approach for spinal tuberculosis that is based on deep learning. The approach uses the Mask R-CNN model. Moreover, we modify the original model network by incorporating the ResPath and cbam* to improve the performance metrics, namely\n \n \n m<\/mml:mi>\n A<\/mml:mi>\n \n P<\/mml:mi>\n \n \n small<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:inline-formula>\n and\n F1-score<\/jats:italic>\n . Meanwhile, other deep learning models such as Faster-RCNN and SSD were also compared. Experimental results demonstrate that the enhanced model can effectively identify spinal tuberculosis lesions, with an\n \n \n m<\/mml:mi>\n A<\/mml:mi>\n \n P<\/mml:mi>\n \n \n small<\/mml:mi>\n <\/mml:mrow>\n <\/mml:mrow>\n <\/mml:msub>\n <\/mml:math>\n <\/jats:inline-formula>\n of 0.9175, surpassing the original model\u2019s 0.8340, and an\n F1-score<\/jats:italic>\n of 0.9335, outperforming the original model\u2019s 0.8657.\n <\/jats:p>","DOI":"10.1177\/08953996241290326","type":"journal-article","created":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T03:19:52Z","timestamp":1735010392000},"page":"120-133","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Mask R-CNN assisted diagnosis of spinal tuberculosis"],"prefix":"10.1177","volume":"33","author":[{"given":"Wenjun","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanfan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-2069-9160","authenticated-orcid":false,"given":"Weijun","family":"Liang","sequence":"additional","affiliation":[{"name":"Department of Respiratory Medicine, Changsha Central Hospital, Changsha, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2024,12,24]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1179\/2045772311Y.0000000023"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41426-018-0106-1"},{"key":"e_1_3_3_4_2","first-page":"221","article-title":"Tuberculosis of the spine","volume":"36","author":"Ferrer MEF","year":"2012","unstructured":"Ferrer MEF, Torres LG, Ram\u00edrez O, et al. Tuberculosis of the spine. Syst Rev Case Ser Int Orthop 2012; 36: 221\u2013231.","journal-title":"Syst Rev Case Ser Int Orthop"},{"key":"e_1_3_3_5_2","doi-asserted-by":"crossref","unstructured":"Cremin BJ Jamieson DH Hoffman EB. CT and MR in the management of advanced spinal tuberculosis. 1993; 23: 298\u2013300.","DOI":"10.1007\/BF02010920"},{"key":"e_1_3_3_6_2","first-page":"129","article-title":"Surgical treatment of spinal tuberculosis complicated with extensive abscess","volume":"34","author":"Brito JSD","year":"2017","unstructured":"Brito JSD, Tirado A, Fernandes P. Surgical treatment of spinal tuberculosis complicated with extensive abscess. Iowa Orthop J 2017; 34: 129\u2013136.","journal-title":"Iowa Orthop J"},{"key":"e_1_3_3_7_2","first-page":"1","article-title":"Primary care quality among different health care structures in Tibet, China","volume":"2015","author":"Wenhua W","year":"2015","unstructured":"Wenhua W, Leiyu S, Aitian Y, et al. Primary care quality among different health care structures in Tibet, China. Biomed Res Int 2015; 2015: 1\u20138.","journal-title":"Biomed Res Int"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2014.09.003"},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","unstructured":"Redmon J Divvala SK Girshick RB et al. You only look\u00a0once: unified real-time object detection. In: IEEE conference on computer vision and pattern recognition 2015 pp.779\u201388. Available from: https:\/\/api.semanticscholar.org\/CorpusID:206594738.","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_3_10_2","unstructured":"Shelhamer E Long J Darrell T. Fully convolutional networks for semantic segmentation. In: IEEE conference on computer vision and pattern recognition 2014 pp.3431\u201340. Available from: https:\/\/api.semanticscholar.org\/CorpusID:1629541."},{"key":"e_1_3_3_11_2","unstructured":"Ronneberger O Fischer P Brox T. U-Net: convolutional networks for biomedical image segmentation. ArXiv. 2015;\u00a0abs\/1505.04597. Available from: https:\/\/api.semanticscholar.org\/CorpusID:3719281."},{"key":"e_1_3_3_12_2","first-page":"2961","article-title":"Mask R-CNN","author":"He K","unstructured":"He K, Gkioxari G, Doll\u2019ar P, et al. Mask R-CNN. In: Proceedings of the IEEE international conference on computer vision 2017, pp.2961\u20132969.","journal-title":"Proceedings of the IEEE international conference on computer vision 2017"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_3_14_2","first-page":"21","article-title":"SSD: Single shot multibox detector","author":"Liu W","unstructured":"Liu W, Anguelov D, Erhan D, et al. SSD: Single shot multibox detector. In: Computer vision\u2013ECCV 2016: 14th European conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, 2016, pp.21\u201337. Springer.","journal-title":"Computer vision\u2013ECCV 2016: 14th European conference"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105814"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-023-03043-5"},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2023.107974"},{"key":"e_1_3_3_18_2","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.12941"},{"key":"e_1_3_3_19_2","article-title":"Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries","author":"Li W","year":"2024","unstructured":"Li W, Gou F, Wu J. Artificial intelligence auxiliary diagnosis and treatment system for breast cancer in developing countries. J X-Ray Sci Technol 2024; 32: 395\u2013413.","journal-title":"J X-Ray Sci Technol"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2024.3351287"},{"key":"e_1_3_3_21_2","first-page":"1","article-title":"User trust and evaluation mechanism based on community classification calculation in opportunistic social networks","volume":"46","author":"Huang J","year":"2024","unstructured":"Huang J, Gou F, Wu J. User trust and evaluation mechanism based on community classification calculation in opportunistic social networks. J Intell Fuzzy Syst 2024; 46: 1\u201318.","journal-title":"J Intell Fuzzy Syst"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2024.3367946"},{"key":"e_1_3_3_23_2","first-page":"3523","article-title":"Image segmentation using deep learning: A survey","volume":"44","author":"Minaee S","year":"2020","unstructured":"Minaee S, Boykov Y, Porikli FM, et al. Image segmentation using deep learning: A survey. IEEE Trans Pattern Anal Mach Intell 2020; 44: 3523\u20133542.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"e_1_3_3_24_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.01.085"},{"key":"e_1_3_3_25_2","first-page":"198","article-title":"RoI pooling based fast multi-domain convolutional neural networks for visual tracking","author":"Qin Y","year":"2016","unstructured":"Qin Y, He S, Zhao Y, et al. RoI pooling based fast multi-domain convolutional neural networks for visual tracking. In: 2016 2nd International conference on artificial intelligence and industrial engineering (AIIE 2016) 2016, pp.198\u2013202.","journal-title":"2016 2nd International conference on artificial intelligence and industrial engineering (AIIE 2016)"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.imed.2022.07.002"},{"key":"e_1_3_3_27_2","doi-asserted-by":"publisher","DOI":"10.1002\/rob.21918"},{"key":"e_1_3_3_28_2","first-page":"357","article-title":"Semantic image segmentation with deep convolutional nets and fully connected CRFs","volume":"12","author":"Chen LC","year":"2014","unstructured":"Chen LC, Papandreou G, Kokkinos I, et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs. Comput Sci 2014; 12: 357\u2013361.","journal-title":"Comput Sci"},{"key":"e_1_3_3_29_2","first-page":"936","article-title":"Feature pyramid networks for object detection","author":"Lin T","unstructured":"Lin T, Doll\u00e1r P, Girshick RB, et al. Feature pyramid networks for object detection. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR) 2016, pp.936\u2013944. Available from: https:\/\/api.semanticscholar.org\/CorpusID:10716717.","journal-title":"2017 IEEE conference on computer vision and pattern recognition (CVPR) 2016"},{"key":"e_1_3_3_30_2","doi-asserted-by":"crossref","unstructured":"He K Zhang X Ren S et al. Deep residual learning for image recognition. In: IEEE conference on computer vision and pattern recognition (CVPR) 2015 pp.770\u2013778. Available from: https:\/\/api.semanticscholar.org\/CorpusID:206594692.","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_31_2","first-page":"1026","article-title":"Delving deep into rectifiers: surpassing human-level performance on imageNet classification","volume":"1","author":"He K","year":"2015","unstructured":"He K, Zhang X, Ren S, et al. Delving deep into rectifiers: surpassing human-level performance on imageNet classification. IEEE Comput Soc 2015; 1: 1026\u20131034.","journal-title":"IEEE Comput Soc"},{"key":"e_1_3_3_32_2","first-page":"249","article-title":"Understanding the difficulty of training deep feedforward neural networks","volume":"9","author":"Glorot X","year":"2010","unstructured":"Glorot X, Bengio Y. Understanding the difficulty of training deep feedforward neural networks. J Mach Learn Res 2010; 9: 249\u2013256.","journal-title":"J Mach Learn Res"},{"key":"e_1_3_3_33_2","first-page":"3","article-title":"Cbam: Convolutional block attention module","author":"Woo S","unstructured":"Woo S, Park J, Lee JY, et al. Cbam: Convolutional block attention module. In: Proceedings of the European conference on computer vision (ECCV) 2018, pp.3\u201319.","journal-title":"Proceedings of the European conference on computer vision (ECCV) 2018"},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10851-019-00922-y"},{"key":"e_1_3_3_35_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.01.013"},{"key":"e_1_3_3_36_2","unstructured":"Pascanu R Mikolov T Bengio Y. How to construct deep recurrent neural networks. In: International conference on learning representations (ICLR) 2013 pp.1\u201314. Available from: https:\/\/arxiv.org\/abs\/1312.6026."},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1214\/aoms\/1177729586"},{"key":"e_1_3_3_38_2","doi-asserted-by":"crossref","unstructured":"Lin T Maire M Belongie S et al. Microsoft COCO: common objects in context. In: European conference on computer vision (ECCV) 2014 pp.741\u2013755. Available from: https:\/\/openaccess.thecvf.com\/content_eccv_2014\/html\/Lin_Microsoft_COCO_Common_2014_ECCV_paper.html.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-7687-1"},{"key":"e_1_3_3_40_2","first-page":"3","article-title":"Neural networks and deep learning","volume":"10","author":"Aggarwal CC","year":"2018","unstructured":"Aggarwal CC. Neural networks and deep learning. Springer 2018; 10: 3\u20134.","journal-title":"Springer"}],"updated-by":[{"DOI":"10.1177\/08953996251346352","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2025,5,23]],"date-time":"2025-05-23T00:00:00Z","timestamp":1747958400000}}],"container-title":["Journal of X-Ray Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08953996241290326","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/08953996241290326","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08953996241290326","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:06:42Z","timestamp":1777673202000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/08953996241290326"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,24]]},"references-count":39,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2025,1]]}},"alternative-id":["10.1177\/08953996241290326"],"URL":"https:\/\/doi.org\/10.1177\/08953996241290326","relation":{},"ISSN":["0895-3996","1095-9114"],"issn-type":[{"value":"0895-3996","type":"print"},{"value":"1095-9114","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,24]]}}}