{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T14:55:33Z","timestamp":1768316133284,"version":"3.49.0"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T00:00:00Z","timestamp":1703635200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Central Leading Local Science and Technology Development Fund","award":["YDZJSX2021C004"],"award-info":[{"award-number":["YDZJSX2021C004"]}]},{"name":"Central Leading Local Science and Technology Development Fund","award":["YDZJSX20231C004"],"award-info":[{"award-number":["YDZJSX20231C004"]}]},{"DOI":"10.13039\/501100004480","name":"Natural Science Foundation of Shanxi Province","doi-asserted-by":"publisher","award":["20210302124554"],"award-info":[{"award-number":["20210302124554"]}],"id":[{"id":"10.13039\/501100004480","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11517-023-02995-9","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T05:02:00Z","timestamp":1703653320000},"page":"1121-1137","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["MCAFNet: multiscale cross-layer attention fusion network for honeycomb lung lesion segmentation"],"prefix":"10.1007","volume":"62","author":[{"given":"Gang","family":"Li","sequence":"first","affiliation":[]},{"given":"Jinjie","family":"Xie","sequence":"additional","affiliation":[]},{"given":"Ling","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mengxia","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Zhichao","family":"Li","sequence":"additional","affiliation":[]},{"given":"Yuanjin","family":"Sun","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,27]]},"reference":[{"key":"2995_CR1","doi-asserted-by":"publisher","first-page":"773","DOI":"10.2214\/AJR.10.4873","volume":"196","author":"H Arakawa","year":"2011","unstructured":"Arakawa H, Honma K (2011) Honeycomb lung: history and current concepts. AJR Am J Roentgenol 196:773\u2013782","journal-title":"AJR Am J Roentgenol"},{"key":"2995_CR2","doi-asserted-by":"publisher","first-page":"921","DOI":"10.1164\/rccm.201607-1385OC","volume":"195","author":"ML Salisbury","year":"2017","unstructured":"Salisbury ML, Lynch DA, van Beek EJ, Kazerooni EA, Guo J, Xia M, Murray S, Anstrom KJ, Yow E, Martinez FJ, Hoffman EA, Flaherty KR, Investigators IP (2017) idiopathic pulmonary fibrosis: the association between the adaptive multiple features method and fibrosis outcomes. Am J Respir Crit Care Med 195:921\u2013929","journal-title":"Am J Respir Crit Care Med"},{"key":"2995_CR3","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0215303","volume":"14","author":"SH Bak","year":"2019","unstructured":"Bak SH, Park HY, Nam JH, Lee HY, Lee JH, Sohn I, Chung MP (2019) Predicting clinical outcome with phenotypic clusters using quantitative CT fibrosis and emphysema features in patients with idiopathic pulmonary fibrosis. PLoS One 14:e0215303","journal-title":"PLoS One"},{"key":"2995_CR4","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1080\/02564602.2014.906861","volume":"31","author":"A Norouzi","year":"2014","unstructured":"Norouzi A, Rahim MSM, Altameem A, Saba T, Rad AE, Rehman A, Uddin M (2014) Medical image segmentation methods, algorithms, and applications. IETE Tech Rev 31:199\u2013213","journal-title":"IETE Tech Rev"},{"key":"2995_CR5","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-Net: convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention \u2013 MICCAI 2015, pp 234\u2013241","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"2018","key":"2995_CR6","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-00889-5_1","volume":"11045","author":"Z Zhou","year":"2018","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J (2018) UNet++: a nested U-Net architecture for medical image segmentation. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support 11045(2018):3\u201311","journal-title":"Deep Learn Med Image Anal Multimodal Learn Clin Decis Support"},{"key":"2995_CR7","volume":"6","author":"MZ Alom","year":"2019","unstructured":"Alom MZ, Yakopcic C, Hasan M, Taha TM, Asari VK (2019) Recurrent residual U-Net for medical image segmentation. J Med Imaging (Bellingham) 6:014006","journal-title":"J Med Imaging (Bellingham)"},{"key":"2995_CR8","doi-asserted-by":"crossref","unstructured":"Chen J, Lu Y, Yu Q, Luo X, Adeli E, Wang Y, Lu L, Yuille AL, Zhou Y (2021) TransUNet: Transformers make strong encoders for medical image segmentation, pp arXiv:2102.04306","DOI":"10.1109\/IGARSS46834.2022.9883628"},{"key":"2995_CR9","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S (2020) An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929"},{"key":"2995_CR10","doi-asserted-by":"crossref","unstructured":"Valanarasu JMJ, Patel VM (2022) UNeXt: MLP-based rapid medical image segmentation network. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI 2022, pp 23\u201333","DOI":"10.1007\/978-3-031-16443-9_3"},{"key":"2995_CR11","doi-asserted-by":"publisher","first-page":"316","DOI":"10.1016\/j.inffus.2022.09.031","volume":"90","author":"I Qureshi","year":"2023","unstructured":"Qureshi I, Yan J, Abbas Q, Shaheed K, Riaz AB, Wahid A, Khan MWJ, Szczuko P (2023) Medical image segmentation using deep semantic-based methods: a review of techniques, applications and emerging trends. Inf Fusion 90:316\u2013352","journal-title":"Inf Fusion"},{"key":"2995_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106960","volume":"159","author":"H Zhang","year":"2023","unstructured":"Zhang H, Zhong X, Li G, Liu W, Liu J, Ji D, Li X, Wu J (2023) BCU-Net: bridging ConvNeXt and U-Net for medical image segmentation. Comput Biol Med 159:106960","journal-title":"Comput Biol Med"},{"key":"2995_CR13","doi-asserted-by":"crossref","unstructured":"Liu Z, Mao H, Wu C-Y, Feichtenhofer C, Darrell T, Xie S (2022) A ConvNet for the 2020s, pp arXiv:2201.03545","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"2995_CR14","doi-asserted-by":"crossref","unstructured":"Qiu C, Liu Z, Song Y, Yin J, Han K, Zhu Y, Liu Y, Sheng VS (2023) RTUNet: residual transformer UNet specifically for pancreas segmentation. Biomed Signal Process Control 79:104173","DOI":"10.1016\/j.bspc.2022.104173"},{"key":"2995_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106891","volume":"158","author":"X Zhang","year":"2023","unstructured":"Zhang X, Liu Y, Guo S, Song Z (2023) EG-Unet: edge-Guided cascaded networks for automated frontal brain segmentation in MR images. Comput Biol Med 158:106891","journal-title":"Comput Biol Med"},{"key":"2995_CR16","doi-asserted-by":"crossref","unstructured":"Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European conference on computer vision (ECCV), pp 801\u2013818","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"2995_CR17","doi-asserted-by":"publisher","unstructured":"Sunnetci KM, Kaba E, Celiker FB, Alkan A (2023) Deep Network-Based Comprehensive Parotid Gland Tumor Detection. Acad Radiol.\u00a0https:\/\/doi.org\/10.1016\/j.acra.2023.04.028","DOI":"10.1016\/j.acra.2023.04.028"},{"key":"2995_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2023.106838","volume":"158","author":"L Jiang","year":"2023","unstructured":"Jiang L, Ou J, Liu R, Zou Y, Xie T, Xiao H, Bai T (2023) RMAU-Net: residual multi-scale attention U-Net for liver and tumor segmentation in CT images. Comput Biol Med 158:106838","journal-title":"Comput Biol Med"},{"key":"2995_CR19","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L-C Chen","year":"2017","unstructured":"Chen L-C, Papandreou G, Kokkinos I, Murphy K, Yuille AL (2017) Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans Pattern Anal Mach Intell 40:834\u2013848","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2995_CR20","doi-asserted-by":"publisher","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. Comput Med Imaging Graph 89:101885","journal-title":"Comput Med Imaging Graph"},{"key":"2995_CR21","doi-asserted-by":"publisher","first-page":"104407","DOI":"10.1016\/j.bspc.2022.104407","volume":"80","author":"R Wu","year":"2023","unstructured":"Wu R, Xin Y, Qian J, Dong Y (2023) A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning. Biomed Signal Process Control 80:104407","journal-title":"Biomed Signal Process Control"},{"key":"2995_CR22","doi-asserted-by":"crossref","unstructured":"Sun Y, Dai D, Zhang Q, Wang Y, Xu S, Lian C (2023) MSCA-Net: multi-scale contextual attention network for skin lesion segmentation. Patt Recog 139:109524","DOI":"10.1016\/j.patcog.2023.109524"},{"key":"2995_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.107147","volume":"226","author":"P Xuan","year":"2022","unstructured":"Xuan P, Jiang B, Cui H, Jin Q, Cheng P, Nakaguchi T, Zhang T, Li C, Ning Z, Guo M, Wang L (2022) Convolutional bi-directional learning and spatial enhanced attentions for lung tumor segmentation. Comput Methods Programs Biomed 226:107147","journal-title":"Comput Methods Programs Biomed"},{"key":"2995_CR24","doi-asserted-by":"crossref","unstructured":"Fan D-P, Ji G-P, Zhou T, Chen G, Fu H, Shen J, Shao L (2020) Pranet: Parallel reverse attention network for polyp segmentation, Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2020: 23rd International Conference, Lima, Peru, October 4\u20138, 2020, Proceedings, Part VI 23, Springer, pp 263\u2013273","DOI":"10.1007\/978-3-030-59725-2_26"},{"key":"2995_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.106516","volume":"153","author":"J Zhang","year":"2023","unstructured":"Zhang J, Qin Q, Ye Q, Ruan T (2023) ST-Unet: Swin Transformer boosted U-Net with cross-layer feature enhancement for medical image segmentation. Comput Biol Med 153:106516","journal-title":"Comput Biol Med"},{"key":"2995_CR26","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. pp arXiv:2103.14030","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"2995_CR27","doi-asserted-by":"crossref","unstructured":"Ates GC, Mohan P, Celik E (2023) Dual cross-attention for medical image segmentation. pp arXiv:2303.17696","DOI":"10.1016\/j.engappai.2023.107139"},{"key":"2995_CR28","doi-asserted-by":"crossref","unstructured":"Chen L, Zhang H, Xiao J, Nie L, Shao J, Liu W, Chua T-S (2017) Sca-cnn: spatial and channel-wise attention in convolutional networks for image captioning. Proceedings of the IEEE conference on computer vision and pattern recognition. pp 5659\u20135667","DOI":"10.1109\/CVPR.2017.667"},{"key":"2995_CR29","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. Proceedings of the IEEE conference on computer vision and pattern recognition. pp 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"2995_CR30","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"2995_CR31","doi-asserted-by":"crossref","unstructured":"Sun J, Zhang X, Li X, Liu R, Wang T (2023) DARMF-UNet: a dual-branch attention-guided refinement network with multi-scale features fusion U-Net for gland segmentation. Comput Biol Med 163:107218","DOI":"10.1016\/j.compbiomed.2023.107218"},{"key":"2995_CR32","doi-asserted-by":"publisher","first-page":"5893","DOI":"10.1109\/TIP.2022.3203223","volume":"31","author":"Q Yu","year":"2022","unstructured":"Yu Q, Qi L, Gao Y, Wang W, Shi Y (2022) Crosslink-Net: double-branch encoder network via fusing vertical and horizontal convolutions for medical image segmentation. IEEE Trans Image Process 31:5893\u20135908","journal-title":"IEEE Trans Image Process"},{"key":"2995_CR33","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1016\/j.patcog.2017.12.016","volume":"77","author":"C Qin","year":"2018","unstructured":"Qin C, Guerrero R, Bowles C, Chen L, Dickie DA, Valdes-Hernandez MdC, Wardlaw J, Rueckert D (2018) A large margin algorithm for automated segmentation of white matter hyperintensity. Pattern Recogn 77:150\u2013159","journal-title":"Pattern Recogn"},{"key":"2995_CR34","doi-asserted-by":"crossref","unstructured":"Gao Z-K, Akben SB, Alkan A (2016) Visual interpretation of biomedical time series using Parzen window-based density-amplitude domain transformation. PloS One 11(9):e0163569","DOI":"10.1371\/journal.pone.0163569"},{"key":"2995_CR35","doi-asserted-by":"crossref","unstructured":"Han Z, Jian M, Wang G-G (2022) ConvUNeXt: an efficient convolution neural network for medical image segmentation. Knowl Based Syst 253:109512","DOI":"10.1016\/j.knosys.2022.109512"},{"key":"2995_CR36","doi-asserted-by":"crossref","unstructured":"Zhao X, Jia H, Pang Y, Lv L, Tian F, Zhang L, Sun W, Lu H (2023) M2SNet: multi-scale in multi-scale subtraction network for medical image segmentation. arXiv preprint arXiv:2303.10894","DOI":"10.1016\/j.bspc.2023.105330"},{"key":"2995_CR37","unstructured":"Jun M, Cheng G, Yixin W, Xingle A, Jiantao G, Ziqi Y, Minqing Z, Xin L, Xueyuan D, Shucheng C (2020) Covid-19 ct lung and infection segmentation dataset. 2020, Verson"},{"key":"2995_CR38","doi-asserted-by":"crossref","unstructured":"Jha D, Smedsrud PH, Riegler MA, Halvorsen P, de Lange T, Johansen D, Johansen HD (2020) Kvasir-seg: a segmented polyp dataset, MultiMedia Modeling: 26th International Conference, MMM 2020, Daejeon, South Korea, January 5\u20138, 2020, Proceedings, Part II 26, Springer, pp 451-462","DOI":"10.1007\/978-3-030-37734-2_37"},{"key":"2995_CR39","doi-asserted-by":"publisher","first-page":"99","DOI":"10.1016\/j.compmedimag.2015.02.007","volume":"43","author":"J Bernal","year":"2015","unstructured":"Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, Gil D, Rodr\u00edguez C, Vilari\u00f1o F (2015) WM-DOVA maps for accurate polyp highlighting in colonoscopy: validation vs. saliency maps from physicians. Comput Med Imaging Graph 43:99\u2013111","journal-title":"Comput Med Imaging Graph"},{"key":"2995_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2017\/4037190","volume":"2017","author":"D V\u00e1zquez","year":"2017","unstructured":"V\u00e1zquez D, Bernal J, S\u00e1nchez FJ, Fern\u00e1ndez-Esparrach G, L\u00f3pez AM, Romero A, Drozdzal M, Courville A (2017) A benchmark for endoluminal scene segmentation of colonoscopy images. J Healthc Eng 2017:1\u20139","journal-title":"J Healthc Eng"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02995-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02995-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02995-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,19]],"date-time":"2024-03-19T03:19:53Z","timestamp":1710818393000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02995-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,27]]},"references-count":40,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["2995"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02995-9","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,27]]},"assertion":[{"value":"29 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 December 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 December 2023","order":3,"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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}