{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,1,17]],"date-time":"2024-01-17T06:46:13Z","timestamp":1705473973248},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"National Nature Science Foundation of China grants","award":["61901214","U20A20389"],"award-info":[{"award-number":["61901214","U20A20389"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2022,8]]},"DOI":"10.1007\/s11517-022-02598-w","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T06:02:24Z","timestamp":1654495344000},"page":"2201-2216","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Efficient lower-limb segmentation for large-scale volumetric CT by using projection view and voxel group attention"],"prefix":"10.1007","volume":"60","author":[{"given":"Fang","family":"Chen","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanting","family":"Xie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peng","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhe","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daoqiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongen","family":"Liao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,6]]},"reference":[{"issue":"1","key":"2598_CR1","doi-asserted-by":"publisher","first-page":"160","DOI":"10.1080\/24699322.2016.1240235","volume":"21","author":"GY Wang","year":"2016","unstructured":"Wang GY, Huang WJ, Song Q et al (2016) Computer-assisted virtual preoperative planning in orthopedic surgery for acetabular fractures based on actual computed tomography data [J]. Comput Assisted Surg 21(1):160\u2013165","journal-title":"Comput Assisted Surg"},{"key":"2598_CR2","doi-asserted-by":"crossref","unstructured":"F Chen, Z Zhao, J Liu, et al 2020 Automatic estimation of morphological characteristics of proximal tibia for precise plate treatment using model matching [J]. Computerized Medical Imaging and Graphics, 101714","DOI":"10.1016\/j.compmedimag.2020.101714"},{"key":"2598_CR3","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1016\/j.medengphy.2019.08.005","volume":"72","author":"F Picard","year":"2019","unstructured":"Picard F, Deakin AH, Riches PE et al (2019) Computer assisted orthopaedic surgery: past, present and future [J]. Med Eng Phys 72:55\u201365","journal-title":"Med Eng Phys"},{"key":"2598_CR4","doi-asserted-by":"publisher","first-page":"103797","DOI":"10.1016\/j.compbiomed.2020.103797","volume":"122","author":"CA Pe\u00f1a-Sol\u00f3rzano","year":"2020","unstructured":"Pe\u00f1a-Sol\u00f3rzano CA, Albrecht DW, Bassed RB et al (2020) Semi-supervised labelling of the femur in a whole-body post-mortem CT database using deep learning [J]. Comput Biol Med 122:103797","journal-title":"Comput Biol Med"},{"issue":"2","key":"2598_CR5","first-page":"63","volume":"36","author":"HR Roth","year":"2018","unstructured":"Roth HR, Shen C, Oda H et al (2018) Deep learning and its application to medical image segmentation [J]. Medical Imaging Technology 36(2):63\u201371","journal-title":"Medical Imaging Technology"},{"key":"2598_CR6","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1016\/j.medengphy.2017.10.008","volume":"51","author":"M Van Eijnatten","year":"2018","unstructured":"Van Eijnatten M, van Dijk R, Dobbe J et al (2018) CT image segmentation methods for bone used in medical additive manufacturing [J]. Med Eng Phys 51:6\u201316","journal-title":"Med Eng Phys"},{"key":"2598_CR7","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1016\/j.compbiomed.2018.10.012","volume":"103","author":"J Minnema","year":"2018","unstructured":"Minnema J, van Eijnatten M, Kouw W et al (2018) CT image segmentation of bone for medical additive manufacturing using a convolutional neural network [J]. Comput Biol Med 103:130\u2013139","journal-title":"Comput Biol Med"},{"key":"2598_CR8","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp S S, et al (2016) 3D U-Net: learning dense volumetric segmentation from sparse annotation. [C]\/\/International conference on medical image computing and computer-assisted intervention. Springer, Cham, 424\u2013432.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"2598_CR9","doi-asserted-by":"crossref","unstructured":"F Milletari, N Navab, S A Ahmadi (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. [C]\/\/2016 fourth international conference on 3D vision (3DV). IEEE, 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"key":"2598_CR10","doi-asserted-by":"crossref","unstructured":"D Im, D Han, S Choi, et al (2020) DT-CNN: an energy-efficient dilated and transposed convolutional neural network processor for region of interest based image segmentation [J]. IEEE Transactions on Circuits and Systems I: Regular Papers","DOI":"10.1109\/TCSI.2020.2991189"},{"key":"2598_CR11","doi-asserted-by":"publisher","first-page":"100297","DOI":"10.1016\/j.imu.2020.100297","volume":"18","author":"IRI Haque","year":"2020","unstructured":"Haque IRI, Neubert J (2020) Deep learning approaches to biomedical image segmentation [J]. Informatics in Medicine Unlocked 18:100297","journal-title":"Informatics in Medicine Unlocked"},{"key":"2598_CR12","doi-asserted-by":"crossref","unstructured":"Valindria V V, Lavdas I, Cerrolaza J, et al (2018) Small organ segmentation in whole-body MRI using a two-stage FCN and weighting schemes. [C]\/\/International Workshop on machine learning in medical imaging. Springer, Cham, 346\u2013354","DOI":"10.1007\/978-3-030-00919-9_40"},{"key":"2598_CR13","doi-asserted-by":"crossref","unstructured":"Wang C, Oda M, Mori K (2020) Organ segmentation from full-size CT images using memory-efficient FCN. [C]\/\/Medical Imaging 2020: Computer-Aided Diagnosis. Int Soc Opt Photon, 11314 113140I","DOI":"10.1117\/12.2551024"},{"key":"2598_CR14","doi-asserted-by":"crossref","unstructured":"ZhangF, Zhu X, Ye M (2019) Fast human pose estimation. [C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 3517\u20133526.","DOI":"10.1109\/CVPR.2019.00363"},{"issue":"4","key":"2598_CR15","doi-asserted-by":"publisher","first-page":"582","DOI":"10.1007\/s10278-019-00227-x","volume":"32","author":"MH Hesamian","year":"2019","unstructured":"Hesamian MH, Jia W, He X et al (2019) Deep learning techniques for medical image segmentation: achievements and challenges [J]. J Digit Imaging 32(4):582\u2013596","journal-title":"J Digit Imaging"},{"key":"2598_CR16","doi-asserted-by":"crossref","unstructured":"ZhongZ, Lin ZQ, Bidart R, et al (2020) Squeeze-and-attention networks for semantic segmentation. [C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 13065\u201313074.","DOI":"10.1109\/CVPR42600.2020.01308"},{"key":"2598_CR17","doi-asserted-by":"crossref","unstructured":"PengC, Zhang X, G Yu, et al (2017) Large kernel matters--improve semantic segmentation by global convolutional network. [C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 4353\u20134361.","DOI":"10.1109\/CVPR.2017.189"},{"key":"2598_CR18","unstructured":"Ypsilantis PP, Montana G (2016) Recurrent convolutional networks for pulmonary nodule detection in CT imaging [J]. arXiv preprint arXiv:1609.09143"},{"key":"2598_CR19","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1016\/j.media.2018.01.006","volume":"45","author":"HR Roth","year":"2018","unstructured":"Roth HR, Lu L, Lay N et al (2018) Spatial aggregation of holistically-nested convolutional neural networks for automated pancreas localization and segmentation [J]. Med Image Anal 45:94\u2013107","journal-title":"Med Image Anal"},{"key":"2598_CR20","doi-asserted-by":"crossref","unstructured":"Yu Q, Xie L, Wang Y, et al (2018) Recurrent saliency transformation network: Incorporating multi-stage visual cues for small organ segmentation. [C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 8280\u20138289.","DOI":"10.1109\/CVPR.2018.00864"},{"key":"2598_CR21","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ejrad.2019.01.028","volume":"113","author":"SL Belal","year":"2019","unstructured":"Belal SL, Sadik M, Kaboteh R et al (2019) Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET\/CT-based 3D quantification of skeletal metastases [J]. Eur J Radiol 113:89\u201395","journal-title":"Eur J Radiol"},{"key":"2598_CR22","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.neunet.2020.01.005","volume":"124","author":"X Li","year":"2020","unstructured":"Li X, Gong Z, Yin H et al (2020) A 3d deep supervised densely network for small organs of human temporal bone segmentation in ct images [J]. Neural Netw 124:75\u201385","journal-title":"Neural Netw"},{"key":"2598_CR23","doi-asserted-by":"crossref","unstructured":"Chen H, Dou Q, Wang X, et al (2016) 3D fully convolutional networks for intervertebral disc localization and segmentation. [C]\/\/International Conference on Medical Imaging and Augmented Reality. Springer, Cham, 375\u2013382.","DOI":"10.1007\/978-3-319-43775-0_34"},{"key":"2598_CR24","first-page":"730","volume":"1","author":"C Wang","year":"2019","unstructured":"Wang C, Guo Y, Chen W et al (2019) Fully automatic intervertebral disc segmentation using multimodal 3D U-Net. [C]\/\/2019 IEEE 43rd Annual Computer Software and Applications Conference (COMPSAC). IEEE 1:730\u2013739","journal-title":"IEEE"},{"key":"2598_CR25","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.ejmp.2019.12.014","volume":"69","author":"JCG S\u00e1nchez","year":"2020","unstructured":"S\u00e1nchez JCG, Magnusson M, Sandborg M et al (2020) Segmentation of bones in medical dual-energy computed tomography volumes using the 3D U-Net [J]. Physica Med 69:241\u2013247","journal-title":"Physica Med"},{"key":"2598_CR26","unstructured":"Leydon P, O'Connell M, Greene D, et al (2020) Bone segmentation in contrast enhanced whole-body computed tomograph. [J]. arXiv preprint arXiv:2008.05223"},{"key":"2598_CR27","unstructured":"Zhao C, Keyak JH, Tang J, et al (2020) A deep learning-based method for automatic segmentation of proximal femur from quantitative computed tomography images [J]. arXiv preprint arXiv:2006.05513"},{"key":"2598_CR28","doi-asserted-by":"crossref","unstructured":"Zhou A, Zhao Q, Zhu J (2019) Automatic segmentation algorithm of femur and tibia based on Vnet-C network. [C]\/\/2019 Chinese Automation Congress (CAC). IEEE, 3066\u20133069","DOI":"10.1109\/CAC48633.2019.8996989"},{"key":"2598_CR29","doi-asserted-by":"crossref","unstructured":"Br\u00fcgger R, Baumgartner CF, Konukoglu E (2019) A partially reversible U-Net for memory-efficient volumetric image segmentation. [C]\/\/International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Cham, 429\u2013437","DOI":"10.1007\/978-3-030-32248-9_48"},{"key":"2598_CR30","doi-asserted-by":"crossref","unstructured":"Cheng X, Jiang Z, Sun Q, et al (2019) Memory-efficient cascade 3D U-Net for brain tumor segmentation. [C]\/\/International MICCAI Brainlesion Workshop. Springer, Cham, 242\u2013253","DOI":"10.1007\/978-3-030-46640-4_23"},{"key":"2598_CR31","doi-asserted-by":"crossref","unstructured":"Frey M, Nau M (2019) Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net. [C]\/\/International MICCAI Brainlesion Workshop. Springer, Cham, 388\u2013396","DOI":"10.1007\/978-3-030-46640-4_37"},{"key":"2598_CR32","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, et al (2018) Non-local neural networks. [C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 7794\u20137803.","DOI":"10.1109\/CVPR.2018.00813"},{"key":"2598_CR33","doi-asserted-by":"crossref","unstructured":"He J, Deng Z, Zhou L, et al (2019) Adaptive pyramid context network for semantic segmentation. [C]\/\/Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 7519\u20137528.","DOI":"10.1109\/CVPR.2019.00770"},{"key":"2598_CR34","unstructured":"Vaswani A, Shazeer N, Parmar N, et al (2017) Attention is all you need. [C]\/\/Advances in neural information processing systems. 5998\u20136008"},{"key":"2598_CR35","unstructured":"Zhang H, Goodfellow I, Metaxas D, et al (2019) Self-attention generative adversarial networks. [C]\/\/International Conference on Machine Learning. PMLR 7354\u20137363."},{"key":"2598_CR36","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: deep learning with depthwise separable convolutions. [C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 1251\u20131258.","DOI":"10.1109\/CVPR.2017.195"},{"key":"2598_CR37","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1016\/j.compbiomed.2017.05.018","volume":"87","author":"S Belharbi","year":"2017","unstructured":"Belharbi S, Chatelain C, H\u00e9rault R et al (2017) Spotting L3 slice in CT scans using deep convolutional network and transfer learning [J]. Comput Biol Med 87:95\u2013103","journal-title":"Comput Biol Med"}],"container-title":["Medical &amp; Biological Engineering &amp; Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02598-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-022-02598-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-022-02598-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,19]],"date-time":"2022-07-19T03:44:29Z","timestamp":1658202269000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-022-02598-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":37,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2022,8]]}},"alternative-id":["2598"],"URL":"https:\/\/doi.org\/10.1007\/s11517-022-02598-w","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,6]]},"assertion":[{"value":"18 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 April 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable (see ethics approval).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}