{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T01:49:11Z","timestamp":1787017751514,"version":"build-2736575974"},"reference-count":43,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T00:00:00Z","timestamp":1660780800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012246","name":"Priority Academic Program Development of Jiangsu Higher Education Institutions","doi-asserted-by":"publisher","award":["SYSD2019152"],"award-info":[{"award-number":["SYSD2019152"]}],"id":[{"id":"10.13039\/501100012246","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Collaborative Innovation Center of Novel Software Technology and Industrialization","award":["SYSD2019152"],"award-info":[{"award-number":["SYSD2019152"]}]},{"name":"Suzhou Science and Technology Bureau","award":["SYSD2019152"],"award-info":[{"award-number":["SYSD2019152"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational and Mathematical Methods in Medicine"],"published-print":{"date-parts":[[2022,8,18]]},"abstract":"<jats:p>Rib fractures are common injuries caused by chest trauma, which may cause serious consequences. It is essential to diagnose rib fractures accurately. Low-dose thoracic computed tomography (CT) is commonly used for rib fracture diagnosis, and convolutional neural network- (CNN-) based methods have assisted doctors in rib fracture diagnosis in recent years. However, due to the lack of rib fracture data and the irregular, various shape of rib fractures, it is difficult for CNN-based methods to extract rib fracture features. As a result, they cannot achieve satisfying results in terms of accuracy and sensitivity in detecting rib fractures. Inspired by the attention mechanism, we proposed the CFSG U-Net for rib fracture detection. The CSFG U-Net uses the U-Net architecture and is enhanced by a dual-attention module, including a channel-wise fusion attention module (CFAM) and a spatial-wise group attention module (SGAM). CFAM uses the channel attention mechanism to reweight the feature map along the channel dimension and refine the U-Net\u2019s skip connections. SGAM uses the group technique to generate spatial attention to adjust feature maps in the spatial dimension, which allows the spatial attention module to capture more fine-grained semantic information. To evaluate the effectiveness of our proposed methods, we established a rib fracture dataset in our research. The experimental results on our dataset show that the maximum sensitivity of our proposed method is 89.58%, and the average FROC score is 81.28%, which outperforms the existing rib fracture detection methods and attention modules.<\/jats:p>","DOI":"10.1155\/2022\/8945423","type":"journal-article","created":{"date-parts":[[2022,8,18]],"date-time":"2022-08-18T22:20:06Z","timestamp":1660861206000},"page":"1-13","source":"Crossref","is-referenced-by-count":11,"title":["Rib Fracture Detection with Dual-Attention Enhanced U-Net"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8832-8873","authenticated-orcid":true,"given":"Zhengyin","family":"Zhou","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9444-5531","authenticated-orcid":true,"given":"Zhihui","family":"Fu","sequence":"additional","affiliation":[{"name":"Department of Radiology, Suzhou TCM Hospital Affiliated to Nanjing University of Chinese Medicine, Suzhou 215000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8276-7640","authenticated-orcid":true,"given":"Juncheng","family":"Jia","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Soochow University, Suzhou 215006, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1989-1360","authenticated-orcid":true,"given":"Jun","family":"Lv","sequence":"additional","affiliation":[{"name":"School of Computer and Control Engineering, Yantai University, Yantai 264005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/s40140-022-00530-4"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1097\/BOT.0000000000001264"},{"key":"3","doi-asserted-by":"publisher","DOI":"10.1259\/bjr.20200870"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinimag.2021.11.008"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.1259\/bjr\/28575455"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8512469"},{"key":"7","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/7517313"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2020.00808"},{"key":"9","article-title":"Yolox: exceeding yolo series in 2021","author":"Z. Ge","year":"2021"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2019.00925"},{"key":"11","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2021.104290"},{"key":"12","first-page":"630","article-title":"Identity mappings in deep residual networks","volume-title":"European Conference on Computer Vision","author":"K. He","year":"2016"},{"issue":"2","key":"13","doi-asserted-by":"crossref","first-page":"1201","DOI":"10.1609\/aaai.v35i2.16207","article-title":"Voxel r-cnn: towards high performance voxel-based 3d object detection","volume":"35","author":"J. Deng","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"14","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2020.105866"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2021.101886"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102287"},{"key":"17","first-page":"311","article-title":"3D MRI brain tumor segmentation using autoencoder regularization","volume-title":"International MICCAI Brain lesion Workshop","author":"A. Myronenko","year":"2018"},{"key":"18","first-page":"118","article-title":"nnu-net for brain tumor segmentation","volume-title":"International MICCAI Brainlesion Workshop","author":"F. Isensee","year":"2020"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-021-90428-8"},{"key":"20","doi-asserted-by":"publisher","DOI":"10.1155\/2021\/5528622"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1038\/s41591-020-01174-9"},{"key":"22","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2020.12.004"},{"key":"23","first-page":"105","article-title":"Scrfd: spatial coherence based rib fracture detection","author":"M. Chen"},{"key":"24","doi-asserted-by":"publisher","DOI":"10.3348\/kjr.2019.0651"},{"key":"25","first-page":"91","article-title":"Faster r-cnn: towards real-time object detection with region proposal networks","volume":"28","author":"S. Ren","year":"2015","journal-title":"Advances in Neural Information Processing Systems"},{"key":"26","doi-asserted-by":"publisher","DOI":"10.1016\/j.ebiom.2020.103106"},{"key":"27","first-page":"234","article-title":"U-net: convolutional networks for biomedical image segmentation","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"O. Ronneberger","year":"2015"},{"key":"28","doi-asserted-by":"publisher","DOI":"10.1007\/s00256-021-03709-8"},{"key":"29","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2019.08.025"},{"key":"30","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i3.20144"},{"key":"31","first-page":"3","article-title":"Cbam: convolutional block attention module","author":"S. Woo"},{"key":"32","article-title":"Bam: bottleneck attention module","author":"J. Park","year":"2018"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2006.01.015"},{"key":"34","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2016.90"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-46976-8_19"},{"key":"36","doi-asserted-by":"publisher","DOI":"10.1109\/cvprw.2017.156"},{"issue":"2","key":"37","article-title":"Imagenet classification with deep convolutional neural networks","volume":"25","author":"A. Krizhevsky","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"38","doi-asserted-by":"publisher","DOI":"10.1109\/iccv.2015.123"},{"key":"39","first-page":"424","article-title":"3d u-net: learning dense volumetric segmentation from sparse annotation","volume-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","author":"\u00d6. \u00c7i\u00e7ek","year":"2016"},{"key":"40","article-title":"Attention u-net: learning where to look for the pancreas","author":"O. Oktay","year":"2018"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1109\/itme.2018.00080"},{"key":"42","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr.2018.00745"},{"key":"43","doi-asserted-by":"publisher","DOI":"10.1109\/cvpr42600.2020.01155"}],"container-title":["Computational and Mathematical Methods in Medicine"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2022\/8945423.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2022\/8945423.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/cmmm\/2022\/8945423.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,2,15]],"date-time":"2023-02-15T01:50:52Z","timestamp":1676425852000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/cmmm\/2022\/8945423\/"}},"subtitle":[],"editor":[{"given":"Kuruva","family":"Lakshmanna","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2022,8,18]]},"references-count":43,"alternative-id":["8945423","8945423"],"URL":"https:\/\/doi.org\/10.1155\/2022\/8945423","relation":{},"ISSN":["1748-6718","1748-670X"],"issn-type":[{"value":"1748-6718","type":"electronic"},{"value":"1748-670X","type":"print"}],"subject":[],"published":{"date-parts":[[2022,8,18]]}}}