{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T15:54:53Z","timestamp":1781279693105,"version":"3.54.1"},"reference-count":55,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"funder":[{"DOI":"10.13039\/501100001667","name":"European Research Consortium for Informatics and Mathematics","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001667","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Future Generation Computer Systems"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1016\/j.future.2020.07.045","type":"journal-article","created":{"date-parts":[[2020,7,30]],"date-time":"2020-07-30T12:29:01Z","timestamp":1596112141000},"page":"185-194","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":59,"special_numbering":"C","title":["SEENS: Nuclei segmentation in Pap smear images with selective edge enhancement"],"prefix":"10.1016","volume":"114","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5060-9223","authenticated-orcid":false,"given":"Meng","family":"Zhao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9301-5989","authenticated-orcid":false,"given":"Hao","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaokang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Ning","family":"Dai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuguo","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marius","family":"Pedersen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.future.2020.07.045_b1","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.future.2019.10.043","article-title":"HealthFog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments","volume":"104","author":"Tuli","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.future.2020.07.045_b2","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1109\/RBME.2018.2848518","article-title":"Introduction to the special section: convergence of automation technology, biomedical engineering, and health informatics toward the healthcare 4.0","volume":"11","author":"Pang","year":"2018","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"5","key":"10.1016\/j.future.2020.07.045_b3","first-page":"99","article-title":"Big data analytics for large-scale wireless networks: Challenges and opportunities","volume":"52","author":"Dai","year":"2019","journal-title":"ACM Comput. Surv."},{"key":"10.1016\/j.future.2020.07.045_b4","doi-asserted-by":"crossref","first-page":"948","DOI":"10.1016\/j.future.2019.07.058","article-title":"Internet of knowledge","volume":"102","author":"Choi","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.future.2020.07.045_b5","article-title":"Machine learning for assisting cervical cancer diagnosis: An ensemble approach","author":"Lu","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"issue":"7553","key":"10.1016\/j.future.2020.07.045_b6","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"Hinton","year":"2015","journal-title":"Nature"},{"issue":"4","key":"10.1016\/j.future.2020.07.045_b7","doi-asserted-by":"crossref","first-page":"2621","DOI":"10.1109\/TII.2019.2941142","article-title":"Exploring unsupervised learning techniques for the Internet of Things","volume":"16","author":"Casolla","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.future.2020.07.045_b8","article-title":"ADTT: A highly-efficient distributed tensor-train decomposition method for IIoT big data","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.future.2020.07.045_b9","article-title":"A tensor-based multi-attributes visual feature recognition method for industrial intelligence","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"S4","key":"10.1016\/j.future.2020.07.045_b10","doi-asserted-by":"crossref","first-page":"1406","DOI":"10.1002\/cncr.2820710405","article-title":"Cervical (Pap) smear: new directions","volume":"71","author":"Koss","year":"1993","journal-title":"Cancer"},{"key":"10.1016\/j.future.2020.07.045_b11","doi-asserted-by":"crossref","first-page":"643","DOI":"10.1016\/j.future.2019.09.015","article-title":"Cervical cancer classification using convolutional neural networks and extreme learning machines","volume":"102","author":"Ghoneim","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.future.2020.07.045_b12","article-title":"Automatic fuzzy clustering framework for image segmentation","author":"Lei","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"issue":"11","key":"10.1016\/j.future.2020.07.045_b13","doi-asserted-by":"crossref","first-page":"5510","DOI":"10.1109\/TIP.2019.2920514","article-title":"Adaptive morphological reconstruction for seeded image segmentation","volume":"28","author":"Lei","year":"2019","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.future.2020.07.045_b14","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1186\/s12859-017-1604-1","article-title":"Generalizing cell segmentation and quantification","volume":"18","author":"Wang","year":"2017","journal-title":"BMC Bioinformatics"},{"issue":"7","key":"10.1016\/j.future.2020.07.045_b15","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1002\/cyto.a.23686","article-title":"Cell segmentation for image cytometry: Advances, insufficiencies, and challenges","volume":"95","author":"Wang","year":"2019","journal-title":"Cytometry A"},{"key":"10.1016\/j.future.2020.07.045_b16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.sigpro.2015.11.011","article-title":"Automatic cell nuclei segmentation and classification of breast cancer histopathology images","volume":"122","author":"Wang","year":"2016","journal-title":"Signal Process."},{"key":"10.1016\/j.future.2020.07.045_b17","article-title":"Nucleus and cytoplasm segmentation in microscopic images using K-means clustering and region growing","volume":"4","author":"Sarrafzadeh","year":"2015","journal-title":"Adv. Biomed. Res."},{"key":"10.1016\/j.future.2020.07.045_b18","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/RBME.2016.2515127","article-title":"Robust nucleus\/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review","volume":"9","author":"Xing","year":"2016","journal-title":"IEEE Rev. Biomed. Eng."},{"issue":"1","key":"10.1016\/j.future.2020.07.045_b19","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1109\/TMI.2016.2606380","article-title":"Accurate cervical cell segmentation from overlapping clumps in pap smear images","volume":"36","author":"Song","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"10","key":"10.1016\/j.future.2020.07.045_b20","doi-asserted-by":"crossref","first-page":"936","DOI":"10.1002\/cyto.a.22702","article-title":"Automatic single cell segmentation on highly multiplexed tissue images","volume":"87","author":"Sch\u00fcffler","year":"2015","journal-title":"Cytometry A"},{"issue":"9","key":"10.1016\/j.future.2020.07.045_b21","doi-asserted-by":"crossref","first-page":"2044","DOI":"10.1109\/TMI.2018.2815013","article-title":"Multi-pass fast watershed for accurate segmentation of overlapping cervical cells","volume":"37","author":"Tareef","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.future.2020.07.045_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.bspc.2019.101575","article-title":"Split and merge watershed: A two-step method for cell segmentation in fluorescence microscopy images","volume":"53","author":"Gamarra","year":"2019","journal-title":"Biomed. Signal Process. Control"},{"key":"10.1016\/j.future.2020.07.045_b23","series-title":"2015 International Conference on Information Processing (ICIP)","first-page":"435","article-title":"Detection of leukemia in microscopic white blood cell images","author":"Khobragade","year":"2015"},{"key":"10.1016\/j.future.2020.07.045_b24","doi-asserted-by":"crossref","first-page":"32412","DOI":"10.1038\/srep32412","article-title":"Accurate morphology preserving segmentation of overlapping cells based on active contours","volume":"6","author":"Molnar","year":"2016","journal-title":"Sci. Rep."},{"issue":"12","key":"10.1016\/j.future.2020.07.045_b25","doi-asserted-by":"crossref","first-page":"2913","DOI":"10.1109\/TBME.2017.2690863","article-title":"Dual-channel active contour model for megakaryocytic cell segmentation in bone marrow trephine histology images","volume":"64","author":"Song","year":"2017","journal-title":"IEEE Trans. Biomed. Eng."},{"issue":"11","key":"10.1016\/j.future.2020.07.045_b26","doi-asserted-by":"crossref","first-page":"4639","DOI":"10.1167\/iovs.18-24734","article-title":"Cone photoreceptor cell segmentation and diameter measurement on adaptive optics images using circularly constrained active contour model","volume":"59","author":"Liu","year":"2018","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"10.1016\/j.future.2020.07.045_b27","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1016\/j.patcog.2017.06.021","article-title":"Automated segmentation of overlapped nuclei using concave point detection and segment grouping","volume":"71","author":"Zhang","year":"2017","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.future.2020.07.045_b28","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.media.2017.02.009","article-title":"Robust detection and segmentation of cell nuclei in biomedical images based on a computational topology framework","volume":"38","author":"Rojas-Moraleda","year":"2017","journal-title":"Med. Image Anal."},{"issue":"3","key":"10.1016\/j.future.2020.07.045_b29","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s10544-017-0208-x","article-title":"Multifractal-based nuclei segmentation in fish images","volume":"19","author":"Reljin","year":"2017","journal-title":"Biomed. Microdev."},{"issue":"11","key":"10.1016\/j.future.2020.07.045_b30","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/83.469936","article-title":"Texture classification and segmentation using wavelet frames","volume":"4","author":"Unser","year":"1995","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"10.1016\/j.future.2020.07.045_b31","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/bit.26064","article-title":"Monitoring of adherent live cells morphology using the undecimated wavelet transform multivariate image analysis (UWT-MIA)","volume":"114","author":"Juneau","year":"2017","journal-title":"Biotechnol. Bioeng."},{"issue":"1","key":"10.1016\/j.future.2020.07.045_b32","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1007\/s10489-013-0509-6","article-title":"Fuzzy mathematical morphology for biological image segmentation","volume":"41","author":"Caponetti","year":"2014","journal-title":"Appl. Intell."},{"issue":"2","key":"10.1016\/j.future.2020.07.045_b33","doi-asserted-by":"crossref","first-page":"513","DOI":"10.3390\/s18020513","article-title":"Recent advances of malaria parasites detection systems based on mathematical morphology","volume":"18","author":"Loddo","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.future.2020.07.045_b34","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.neucom.2014.01.061","article-title":"Accurate segmentation of touching cells in multi-channel microscopy images with geodesic distance based clustering","volume":"149","author":"Chen","year":"2015","journal-title":"Neurocomputing"},{"issue":"1","key":"10.1016\/j.future.2020.07.045_b35","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1007\/s12015-017-9780-y","article-title":"Familial limbal stem cell deficiency: clinical, cytological and genetic characterization","volume":"14","author":"Dudakova","year":"2018","journal-title":"Stem Cell Rev. Rep."},{"key":"10.1016\/j.future.2020.07.045_b36","series-title":"International Conference on Medical Image Computing and Computer-Assisted Intervention","first-page":"234","article-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.future.2020.07.045_b37","first-page":"1","article-title":"Deep learning for cellular image analysis","author":"Moen","year":"2019","journal-title":"Nature Methods"},{"key":"10.1016\/j.future.2020.07.045_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.04.011","article-title":"GRUU-Net: Integrated convolutional and gated recurrent neural network for cell segmentation","author":"Wollmann","year":"2019","journal-title":"Med. Image Anal."},{"issue":"6","key":"10.1016\/j.future.2020.07.045_b39","doi-asserted-by":"crossref","first-page":"1644","DOI":"10.1109\/JBHI.2016.2623421","article-title":"Segmentation of white blood cells image using adaptive location and iteration","volume":"21","author":"Liu","year":"2016","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"1","key":"10.1016\/j.future.2020.07.045_b40","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1109\/JBHI.2018.2803020","article-title":"Cell segmentation based on FOPSO combined with shape information improved intuitionistic FCM","volume":"23","author":"Bai","year":"2018","journal-title":"IEEE J. Biomed. Health Inf."},{"issue":"2","key":"10.1016\/j.future.2020.07.045_b41","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TITB.2010.2087030","article-title":"Automated detection of cell nuclei in pap smear images using morphological reconstruction and clustering","volume":"15","author":"Plissiti","year":"2010","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"10.1016\/j.future.2020.07.045_b42","series-title":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","first-page":"406","article-title":"Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei","author":"Zhang","year":"2017"},{"issue":"6","key":"10.1016\/j.future.2020.07.045_b43","doi-asserted-by":"crossref","first-page":"838","DOI":"10.1016\/j.patrec.2011.01.008","article-title":"Combining shape, texture and intensity features for cell nuclei extraction in pap smear images","volume":"32","author":"Plissiti","year":"2011","journal-title":"Pattern Recognit. Lett."},{"issue":"4","key":"10.1016\/j.future.2020.07.045_b44","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1007\/s40012-013-0028-y","article-title":"A fast and reliable approach to cell nuclei segmentation in PAP stained cervical smears","volume":"1","author":"Byju","year":"2013","journal-title":"CSI Trans. ICT"},{"issue":"10","key":"10.1016\/j.future.2020.07.045_b45","doi-asserted-by":"crossref","first-page":"2421","DOI":"10.1109\/TBME.2015.2430895","article-title":"Accurate segmentation of cervical cytoplasm and nuclei based on multiscale convolutional network and graph partitioning","volume":"62","author":"Song","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.future.2020.07.045_b46","doi-asserted-by":"crossref","unstructured":"I. Muhimmah, R. Kurniawan, . Indrayanti, Automated cervical cell nuclei segmentation using morphological operation and watershed transformation, in: 2012 IEEE International Conference on Computational Intelligence and Cybernetics (CyberneticsCom), 2012, pp. 163\u2013167.","DOI":"10.1109\/CyberneticsCom.2012.6381639"},{"key":"10.1016\/j.future.2020.07.045_b47","doi-asserted-by":"crossref","unstructured":"C.-W. Chang, M.-Y. Lin, H.-J. Harn, Y.-C. Harn, C.-H. Chen, K.-H. Tsai, C.-H. Hwang, Automatic segmentation of abnormal cell nuclei from microscopic image analysis for cervical cancer screening, in: 2009 IEEE 3rd International Conference on Nano\/Molecular Medicine and Engineering, 2009, pp. 77\u201380.","DOI":"10.1109\/NANOMED.2009.5559114"},{"issue":"5","key":"10.1016\/j.future.2020.07.045_b48","doi-asserted-by":"crossref","first-page":"369","DOI":"10.1016\/j.compmedimag.2014.02.001","article-title":"Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts","volume":"38","author":"Zhang","year":"2014","journal-title":"Comput. Med. Imaging Graph."},{"key":"10.1016\/j.future.2020.07.045_b49","doi-asserted-by":"crossref","unstructured":"T. Guan, D. Zhou, W. Fan, K. Peng, C. Xu, X. Cai, Nuclei enhancement and segmentation in color cervical smear images, in: 2014 IEEE International Conference on Robotics and Biomimetics (ROBIO 2014), 2014, pp. 107\u2013112.","DOI":"10.1109\/ROBIO.2014.7090315"},{"key":"10.1016\/j.future.2020.07.045_b50","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.compmedimag.2016.01.002","article-title":"Curvelet initialized level set cell segmentation for touching cells in low contrast images","volume":"49","author":"Kaur","year":"2016","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."},{"issue":"10","key":"10.1016\/j.future.2020.07.045_b51","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/TMI.2018.2841910","article-title":"Corneal endothelial cell segmentation by classifier-driven merging of oversegmented images","volume":"37","author":"Vigueras-Guill\u00e9n","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.future.2020.07.045_b52","doi-asserted-by":"crossref","unstructured":"R. Wang, S.-I. Kamata, Nuclei segmentation of cervical cell images based on intermediate segment qualifier, in: International Conference on Pattern Recognition, 2018.","DOI":"10.1109\/ICPR.2018.8546215"},{"issue":"2","key":"10.1016\/j.future.2020.07.045_b53","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1007\/s11263-013-0620-5","article-title":"Selective search for object recognition","volume":"104","author":"Uijlings","year":"2013","journal-title":"Int. J. Comput. Vis."},{"issue":"2","key":"10.1016\/j.future.2020.07.045_b54","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1109\/83.902291","article-title":"Active contours without edges","volume":"10","author":"Chan","year":"2001","journal-title":"IEEE Trans. Image Process."},{"issue":"6","key":"10.1016\/j.future.2020.07.045_b55","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1109\/TPAMI.1986.4767851","article-title":"A computational approach to edge detection","author":"Canny","year":"1986","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Future Generation Computer Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X20304271?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167739X20304271?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,29]],"date-time":"2025-09-29T04:20:27Z","timestamp":1759119627000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167739X20304271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1]]},"references-count":55,"alternative-id":["S0167739X20304271"],"URL":"https:\/\/doi.org\/10.1016\/j.future.2020.07.045","relation":{},"ISSN":["0167-739X"],"issn-type":[{"value":"0167-739X","type":"print"}],"subject":[],"published":{"date-parts":[[2021,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"SEENS: Nuclei segmentation in Pap smear images with selective edge enhancement","name":"articletitle","label":"Article Title"},{"value":"Future Generation Computer Systems","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.future.2020.07.045","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}