{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T23:46:22Z","timestamp":1740181582121,"version":"3.37.3"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T00:00:00Z","timestamp":1688774400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-023-01905-y","type":"journal-article","created":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T16:01:37Z","timestamp":1688832097000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["DenseRes-Net: An Architecture for Gland Segmentation"],"prefix":"10.1007","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9294-9042","authenticated-orcid":false,"given":"Wangkheirakpam","family":"Reema Devi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sudipta","family":"Roy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Khelchandra","family":"Thongam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chiranjiv","family":"Chingangbam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,7,8]]},"reference":[{"key":"1905_CR1","first-page":"1097","volume":"25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst. 2012;25:1097\u2013105.","journal-title":"Adv Neural Inf Process Syst"},{"issue":"1","key":"1905_CR2","first-page":"3","volume":"35","author":"N Sharma","year":"2010","unstructured":"Sharma N, Aggarwal LM. Automated medical image segmentation techniques. J Med Phys\/Assoc Med Phys India. 2010;35(1):3.","journal-title":"J Med Phys\/Assoc Med Phys India"},{"issue":"2","key":"1905_CR3","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1109\/34.44403","volume":"12","author":"ER Hancock","year":"1990","unstructured":"Hancock ER, Kittler J. Edge-labeling using dictionary-based relaxation. IEEE Trans Pattern Anal Mach Intell. 1990;12(2):165\u201381.","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"3","key":"1905_CR4","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1111\/j.1365-2818.2005.01531.x","volume":"220","author":"H-S Wu","year":"2005","unstructured":"Wu H-S, Xu R, Harpaz N, Burstein D, Gil J. Segmentation of intestinal gland images with iterative region growing. J Microsc. 2005;220(3):190\u2013204.","journal-title":"J Microsc"},{"issue":"11","key":"1905_CR5","doi-asserted-by":"publisher","first-page":"2366","DOI":"10.1109\/TMI.2015.2433900","volume":"34","author":"K Sirinukunwattana","year":"2015","unstructured":"Sirinukunwattana K, Snead DR, Rajpoot NM. A stochastic polygons model for glandular structures in colon histology images. IEEE Trans Med Imaging. 2015;34(11):2366\u201378.","journal-title":"IEEE Trans Med Imaging"},{"issue":"4","key":"1905_CR6","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1002\/cyto.b.20162","volume":"72","author":"R Farjam","year":"2007","unstructured":"Farjam R, Soltanian-Zadeh H, Jafari-Khouzani K, Zoroofi RA. An image analysis approach for automatic malignancy determination of prostate pathological images. Cytom Part B: Clin Cytom: J Int Soc Anal Cytol. 2007;72(4):227\u201340.","journal-title":"Cytom Part B: Clin Cytom: J Int Soc Anal Cytol"},{"key":"1905_CR7","doi-asserted-by":"crossref","unstructured":"Ren J, Sadimin E, Foran DJ, Qi X. Computer aided analysis of prostate histopathology images to support a refined Gleason grading system. In: Medical imaging 2017: image processing, Vol. 10133, International Society for Optics and Photonics; 2017. p. 101331V.","DOI":"10.1117\/12.2253887"},{"key":"1905_CR8","unstructured":"Raza SEA, Cheung L, Epstein D, Pelengaris S, Khan M, Rajpoot NM. Mimo-net: a multi-input multi-output convolutional neural network for cell segmentation in fluorescence microscopy images. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). IEEE; 2017. p. 337\u201340."},{"key":"1905_CR9","doi-asserted-by":"crossref","unstructured":"Roth HR, Lu L, Farag A, Shin H-C, Liu J, Turkbey EB, Summers RM. Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer; 2015. p. 556\u201364.","DOI":"10.1007\/978-3-319-24553-9_68"},{"key":"1905_CR10","doi-asserted-by":"crossref","unstructured":"Chen H, Qi X, Yu L, Heng P-A. DCAN: deep contour-aware networks for accurate gland segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 2487\u201396.","DOI":"10.1109\/CVPR.2016.273"},{"key":"1905_CR11","doi-asserted-by":"crossref","unstructured":"Xu Y, Li Y, Liu M, Wang Y, Lai M, Eric I, Chang C. Gland instance segmentation by deep multichannel side supervision. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer; 2016. p. 496\u2013504.","DOI":"10.1007\/978-3-319-46723-8_57"},{"key":"1905_CR12","doi-asserted-by":"crossref","unstructured":"Yang L, Zhang Y, Chen J, Zhang S, Chen DZ. Suggestive annotation: a deep active learning framework for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer; 2017. p. 399\u2013407.","DOI":"10.1007\/978-3-319-66179-7_46"},{"key":"1905_CR13","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1016\/j.media.2018.12.001","volume":"52","author":"S Graham","year":"2019","unstructured":"Graham S, Chen H, Gamper J, Dou Q, Heng P-A, Snead D, Tsang YW, Rajpoot N. Mild-net: minimal information loss dilated network for gland instance segmentation in colon histology images. Med Image Anal. 2019;52:199\u2013211.","journal-title":"Med Image Anal"},{"key":"1905_CR14","doi-asserted-by":"crossref","unstructured":"Long J, Shelhamer E, Darrell T. Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2015. p. 3431\u201340.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"1905_CR15","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer; 2015. p. 234\u201341.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1905_CR16","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek \u00d6, Abdulkadir A, Lienkamp SS, Brox T, Ronneberger O. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In: International conference on medical image computing and computer-assisted intervention. Berlin: Springer; 2016. p. 424\u201332.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"1905_CR17","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi S-A. V-net: fully convolutional neural networks for volumetric medical image segmentation. In: 2016 Fourth international conference on 3D vision (3DV). IEEE; 2016. p. 565\u201371.","DOI":"10.1109\/3DV.2016.79"},{"key":"1905_CR18","doi-asserted-by":"crossref","unstructured":"Gordienko Y, Gang P, Hui J, Zeng W, Kochura Y, Alienin O, Rokovyi O, Stirenko S. Deep learning with lung segmentation and bone shadow exclusion techniques for chest x-ray analysis of lung cancer. In: International conference on computer science, engineering and education applications. Berlin: Springer; 2018. p. 638\u201347.","DOI":"10.1007\/978-3-319-91008-6_63"},{"key":"1905_CR19","doi-asserted-by":"crossref","unstructured":"Zhou Z, Siddiquee MMR, Tajbakhsh N, Liang J. Unet++: a nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support. Berlin: Springer; 2018. p. 3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"issue":"4","key":"1905_CR20","doi-asserted-by":"publisher","first-page":"1731","DOI":"10.1109\/TCYB.2020.2969046","volume":"51","author":"J Yu","year":"2020","unstructured":"Yu J, Yao J, Zhang J, Yu Z, Tao D. SPRNet: single-pixel reconstruction for one-stage instance segmentation. IEEE Trans Cybern. 2020;51(4):1731\u201342.","journal-title":"IEEE Trans Cybern"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01905-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-023-01905-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-023-01905-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,8]],"date-time":"2023-07-08T16:14:02Z","timestamp":1688832842000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-023-01905-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,8]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["1905"],"URL":"https:\/\/doi.org\/10.1007\/s42979-023-01905-y","relation":{},"ISSN":["2661-8907"],"issn-type":[{"type":"electronic","value":"2661-8907"}],"subject":[],"published":{"date-parts":[[2023,7,8]]},"assertion":[{"value":"25 March 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 February 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 July 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 have no conflicts of interest to declare that they are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of Interest"}}],"article-number":"521"}}