{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T23:29:01Z","timestamp":1782948541472,"version":"3.54.5"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"13","license":[{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T00:00:00Z","timestamp":1647475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61872261"],"award-info":[{"award-number":["61872261"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972274"],"award-info":[{"award-number":["61972274"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Taiyuan Science and Technology Project Plan","award":["XE2020-5-04"],"award-info":[{"award-number":["XE2020-5-04"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,10]]},"DOI":"10.1007\/s10489-021-03038-2","type":"journal-article","created":{"date-parts":[[2022,3,17]],"date-time":"2022-03-17T06:03:37Z","timestamp":1647497017000},"page":"15617-15631","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["DAS-Net: A lung nodule segmentation method based on adaptive dual-branch attention and shadow mapping"],"prefix":"10.1007","volume":"52","author":[{"given":"Shichao","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jina","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ning","family":"Xiao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6690-1819","authenticated-orcid":false,"given":"Yan","family":"Qiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keqin","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juanjuan","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liang","family":"Meng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ping","family":"Song","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,3,17]]},"reference":[{"key":"3038_CR1","unstructured":"W. H. Organization, et al (2020) Latest global cancer data:cancer burden rises to 19.3 million new cases and 10.0 million cancer deaths in 2020, Press release"},{"issue":"5","key":"3038_CR2","doi-asserted-by":"publisher","first-page":"395","DOI":"10.1056\/NEJMoa1102873","volume":"365","author":"NLSTR Team","year":"2011","unstructured":"NLSTR Team (2011) Reduced lung-cancer mortality with low-dose computed tomographic screening. New England Journal of Medicine 365(5):395\u2013409","journal-title":"New England Journal of Medicine"},{"issue":"6","key":"3038_CR3","first-page":"1017","volume":"64","author":"Q Liu","year":"2019","unstructured":"Liu Q (2019) Understanding the global cancer statistics 2018: implications for cancer control. Science China Life Sciences 64(6):1017\u20131020","journal-title":"Science China Life Sciences"},{"issue":"4","key":"3038_CR4","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/S0344-0338(84)80029-1","volume":"178","author":"KW Zieli\u0144ski","year":"1984","unstructured":"Zieli\u0144ski KW, Kulig A, Zieli\u0144ski J (1984) Morphology of the microvascular bed in primary human carcinomas of lung: Part ii. morphometric investigations of microvascular bed of lung tumors. Pathology-Research and Practice 178(4):369\u2013377","journal-title":"Pathology-Research and Practice"},{"issue":"3","key":"3038_CR5","doi-asserted-by":"publisher","first-page":"463","DOI":"10.1016\/j.mcna.2018.12.006","volume":"103","author":"F Nasim","year":"2019","unstructured":"Nasim F, Sabath B, Eapen G (2019) Lung cancer. Medical Clinics of North America 103(3):463\u2013473","journal-title":"Medical Clinics of North America"},{"key":"3038_CR6","doi-asserted-by":"crossref","unstructured":"Savic M, Ma Y, Ramponi G, Du W, Peng Y (2021) Lung nodule segmentation with a region-based fast marching method. Sensors 21(5): 1908","DOI":"10.3390\/s21051908"},{"issue":"5","key":"3038_CR7","doi-asserted-by":"publisher","first-page":"1306","DOI":"10.1007\/s10278-020-00346-w","volume":"33","author":"RJ Suji","year":"2020","unstructured":"Suji RJ, Bhadouria SS, Dhar J, Godfrey WW (2020) Optical flow methods for lung nodule segmentation on lidc-idri images. Journal of Digital Imaging 33(5):1306\u20131324","journal-title":"Journal of Digital Imaging"},{"key":"3038_CR8","doi-asserted-by":"publisher","first-page":"102296","DOI":"10.1016\/j.bspc.2020.102296","volume":"64","author":"SP Pawar","year":"2021","unstructured":"Pawar SP, Talbar SN (2021) Lungseg-net: Lung field segmentation using generative adversarial network. Biomedical Signal Processing and Control 64:102296","journal-title":"Biomedical Signal Processing and Control"},{"key":"3038_CR9","doi-asserted-by":"publisher","first-page":"7255","DOI":"10.1109\/ACCESS.2021.3049379","volume":"9","author":"Z Wu","year":"2021","unstructured":"Wu Z, Zhou Q, Wang F (2021) Coarse-to-fine lung nodule segmentation in ct images with image enhancement and dual-branch network. IEEE Access 9:7255\u20137262","journal-title":"IEEE Access"},{"key":"3038_CR10","doi-asserted-by":"publisher","first-page":"105792","DOI":"10.1016\/j.cmpb.2020.105792","volume":"198","author":"G Pezzano","year":"2021","unstructured":"Pezzano G, Ripoll VR, Radeva P (2021) Cole-cnn: Context-learning convolutional neural network with adaptive loss function for lung nodule segmentation. Computer Methods and Programs in Biomedicine 198:105792","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"6","key":"3038_CR11","doi-asserted-by":"publisher","first-page":"1465","DOI":"10.1007\/s10278-020-00388-0","volume":"33","author":"C Liu","year":"2020","unstructured":"Liu C, Pang M (2020) Extracting lungs from ct images via deep convolutional neural network based segmentation and two-pass contour refinement. Journal of Digital Imaging 33(6):1465\u20131478","journal-title":"Journal of Digital Imaging"},{"issue":"4","key":"3038_CR12","doi-asserted-by":"publisher","first-page":"1151","DOI":"10.1109\/JBHI.2020.3004296","volume":"25","author":"P Sahu","year":"2020","unstructured":"Sahu P, Zhao Y, Bhatia P, Bogoni L, Jerebko A, Qin H (2020) Structure correction for robust volume segmentation in presence of tumors. IEEE Journal of Biomedical and Health Informatics 25(4):1151\u20131162","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"3038_CR13","doi-asserted-by":"publisher","first-page":"104032","DOI":"10.1016\/j.compbiomed.2020.104032","volume":"126","author":"M Nishio","year":"2020","unstructured":"Nishio M, Muramatsu C, Noguchi S, Nakai H, Fujimoto K, Sakamoto R, Fujita H (2020) Attribute-guided image generation of three-dimensional computed tomography images of lung nodules using a generative adversarial network. Computers in Biology and Medicine 126:104032","journal-title":"Computers in Biology and Medicine"},{"key":"3038_CR14","doi-asserted-by":"publisher","first-page":"26457","DOI":"10.1109\/ACCESS.2020.2971542","volume":"8","author":"Y Sun","year":"2020","unstructured":"Sun Y, Tang J, Lei W, He D (2020) 3d segmentation of pulmonary nodules based on multi-view and semi-supervised. IEEE Access 8:26457\u201326467","journal-title":"IEEE Access"},{"issue":"3","key":"3038_CR15","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1007\/s10489-020-01914-x","volume":"51","author":"MH Hesamian","year":"2021","unstructured":"Hesamian MH, Jia W, He X, Wang Q, Kennedy PJ (2021) Synthetic ct images for semi-sequential detection and segmentation of lung nodules. Applied Intelligence 51(3):1616\u20131628","journal-title":"Applied Intelligence"},{"issue":"1","key":"3038_CR16","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1186\/s40644-020-00331-0","volume":"20","author":"X Dong","year":"2020","unstructured":"Dong X, Xu S, Liu Y, Wang A, Saripan MI, Li L, Zhang X, Lu L (2020) Multi-view secondary input collaborative deep learning for lung nodule 3d segmentation. Cancer Imaging 20(1):53\u201366","journal-title":"Cancer Imaging"},{"issue":"2","key":"3038_CR17","doi-asserted-by":"publisher","first-page":"1010","DOI":"10.1007\/s10489-020-01867-1","volume":"51","author":"T Zebin","year":"2021","unstructured":"Zebin T, Rezvy S (2021) Covid-19 detection and disease progression visualization: Deep learning on chest x-rays for classification and coarse localization. Applied Intelligence 51(2):1010\u20131021","journal-title":"Applied Intelligence"},{"key":"3038_CR18","first-page":"234","volume":"2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. International conference on medical image computing and computer-assisted intervention - MICCAI 2015:234\u2013241","journal-title":"International conference on medical image computing and computer-assisted intervention - MICCAI"},{"key":"3038_CR19","doi-asserted-by":"publisher","first-page":"101885","DOI":"10.1016\/j.compmedimag.2021.101885","volume":"89","author":"DT Kushnure","year":"2021","unstructured":"Kushnure DT, Talbar SN (2021) Ms-unet: A multi-scale unet with feature recalibration approach for automatic liver and tumor segmentation in ct images. Computerized Medical Imaging and Graphics 89:101885","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"3038_CR20","first-page":"302","volume":"2019","author":"Y Fang","year":"2019","unstructured":"Fang Y, Chen C, Yuan Y, Tong K-Y (2019) Selective feature aggregation network with area-boundary constraints for polyp segmentation. Medical image computing and computer assisted intervention - MICCAI 2019:302\u2013310","journal-title":"Medical image computing and computer assisted intervention - MICCAI"},{"key":"3038_CR21","doi-asserted-by":"crossref","unstructured":"Perslev M, Dam EB, Pai A, Igel C (2019) One network to segment them all: A general, lightweight system for accurate 3d medical image segmentation. In: Medical image computing and computer assisted intervention - MICCAI 2019. pp 30\u201338","DOI":"10.1007\/978-3-030-32245-8_4"},{"key":"3038_CR22","first-page":"39","volume":"2019","author":"A-M Rickmann","year":"2019","unstructured":"Rickmann A-M, Roy AG, Sarasua I, Navab N, Wachinger C (2019) \u2019project & excite\u2019modules for segmentation of volumetric medical scans. Medical image computing and computer assisted intervention - MICCAI 2019:39\u201347","journal-title":"Medical image computing and computer assisted intervention - MICCAI"},{"key":"3038_CR23","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"issue":"1","key":"3038_CR24","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-69817-y","volume":"10","author":"M Usman","year":"2020","unstructured":"Usman M, Lee B-D, Byon S-S, Kim S-H, Lee B-I, Shin Y-G (2020) Volumetric lung nodule segmentation using adaptive roi with multi-view residual learning. Scientific Reports 10(1):1\u201315","journal-title":"Scientific Reports"},{"issue":"5","key":"3038_CR25","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","volume":"15","author":"Z Zhang","year":"2018","unstructured":"Zhang Z, Liu Q, Wang Y (2018) Road extraction by deep residual u-net. IEEE Geoscience and Remote Sensing Letters 15(5):749\u2013753","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"3038_CR26","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. pp 6000\u20136010"},{"key":"3038_CR27","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR). pp 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"3038_CR28","doi-asserted-by":"crossref","unstructured":"Huang Z, Wang X, Huang L, Huang C, Wei Y, Liu W (2019) Ccnet: Criss-cross attention for semantic segmentation. In: Proceedings of the IEEE\/CVF international conference on computer vision (ICCV). pp 603\u2013612","DOI":"10.1109\/ICCV.2019.00069"},{"key":"3038_CR29","doi-asserted-by":"crossref","unstructured":"Fu J, Liu J, Tian H, Li Y, Bao Y, Fang Z, Lu H (2019) Dual attention network for scene segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR). pp 3141\u20133149","DOI":"10.1109\/CVPR.2019.00326"},{"issue":"1","key":"3038_CR30","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1109\/JBHI.2020.2986926","volume":"25","author":"A Sinha","year":"2020","unstructured":"Sinha A, Dolz J (2020) Multi-scale self-guided attention for medical image segmentation. IEEE Journal of Biomedical and Health Informatics 25(1):121\u2013130","journal-title":"IEEE Journal of Biomedical and Health Informatics"},{"key":"3038_CR31","unstructured":"Park J, Woo S, Lee J-Y, Kweon IS (2018) Bam: Bottleneck attention module. arXiv:1807.06514"},{"key":"3038_CR32","unstructured":"Yu F, Koltun V (2015) Multi-scale context aggregation by dilated convolutions. arXiv:1511.07122"},{"key":"3038_CR33","first-page":"146","volume":"2019","author":"G Zeng","year":"2019","unstructured":"Zeng G, Zheng G (2019) 3d tiled convolution for effective segmentation of volumetric medical images. Medical image computing and computer assisted intervention - MICCAI 2019:146\u2013154","journal-title":"Medical image computing and computer assisted intervention - MICCAI"},{"key":"3038_CR34","doi-asserted-by":"crossref","unstructured":"Han K, Wang Y, Tian Q, Guo J, Xu C, Xu C (2020) Ghostnet: More features from cheap operations. In: 2020 IEEE\/CVF conference on computer vision and pattern recognition (CVPR). pp 1577\u20131586","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"3038_CR35","first-page":"424","volume":"2016","author":"\u00d6 \u00c7i\u00e7ek","year":"2016","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O (2016) 3d u-net: learning dense volumetric segmentation from sparse annotation. Medical image computing and computer-assisted intervention - MICCAI 2016:424\u2013432","journal-title":"Medical image computing and computer-assisted intervention - MICCAI"},{"key":"3038_CR36","unstructured":"Ho J, Kalchbrenner N, Weissenborn D, Salimans T, Axial attention in multidimensional transformers. arXiv:1912.12180"},{"key":"3038_CR37","doi-asserted-by":"publisher","first-page":"101851","DOI":"10.1016\/j.media.2020.101851","volume":"67","author":"H Kervadec","year":"2021","unstructured":"Kervadec H, Bouchtiba J, Desrosiers C, Granger E, Dolz J, Ayed IB (2021) Boundary loss for highly unbalanced segmentation. Medical Image Analysis 67:101851","journal-title":"Medical Image Analysis"},{"issue":"2","key":"3038_CR38","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1118\/1.3528204","volume":"38","author":"SG Armato III","year":"2011","unstructured":"Armato SG III, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA et al (2011) The lung image database consortium (lidc) and image database resource initiative (idri): a completed reference database of lung nodules on ct scans. Medical physics 38(2):915\u2013931","journal-title":"Medical physics"},{"key":"3038_CR39","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A (2016) V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE, pp 565\u2013571","DOI":"10.1109\/3DV.2016.79"},{"key":"3038_CR40","doi-asserted-by":"crossref","unstructured":"Maqsood M, Yasmin S, Mehmood I, Bukhari M, Kim M (2021) An efficient da-net architecture for lung nodule segmentation. Mathematics 9 (13)","DOI":"10.3390\/math9131457"},{"issue":"9","key":"3038_CR41","doi-asserted-by":"publisher","first-page":"4054","DOI":"10.1002\/mp.14248","volume":"47","author":"W Wu","year":"2020","unstructured":"Wu W, Gao L, Duan H, Huang G, Ye X, Nie S (2020) Segmentation of pulmonary nodules in ct images based on 3d-unet combined with three-dimensional conditional random field optimization. Medical Physics 47(9):4054\u20134063","journal-title":"Medical Physics"},{"issue":"11","key":"3038_CR42","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.3390\/sym12111787","volume":"12","author":"Z Xiao","year":"2020","unstructured":"Xiao Z, Liu B, Geng L, Zhang F, Liu Y (2020) Segmentation of lung nodules using improved 3d-unet neural network. Symmetry 12(11):1787","journal-title":"Symmetry"},{"key":"3038_CR43","doi-asserted-by":"publisher","first-page":"105934","DOI":"10.1016\/j.asoc.2019.105934","volume":"86","author":"H Cao","year":"2020","unstructured":"Cao H, Liu H, Song E, Hung C-C, Ma G, Xu X, Jin R, Lu J (2020) Dual-branch residual network for lung nodule segmentation. Applied Soft Computing 86:105934","journal-title":"Applied Soft Computing"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03038-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-03038-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-03038-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,1]],"date-time":"2022-10-01T10:07:01Z","timestamp":1664618821000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-03038-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,17]]},"references-count":43,"journal-issue":{"issue":"13","published-print":{"date-parts":[[2022,10]]}},"alternative-id":["3038"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-03038-2","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,17]]},"assertion":[{"value":"24 November 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 March 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}