{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T15:30:37Z","timestamp":1758123037945,"version":"3.40.2"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T00:00:00Z","timestamp":1711497600000},"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":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-024-18705-y","type":"journal-article","created":{"date-parts":[[2024,3,27]],"date-time":"2024-03-27T03:01:55Z","timestamp":1711508515000},"page":"5025-5050","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Nucleus segmentation from the histopathological images of liver cancer through an efficient deep learning framework"],"prefix":"10.1007","volume":"84","author":[{"family":"Sunesh","sequence":"first","affiliation":[]},{"given":"Jyoti","family":"Tripathi","sequence":"additional","affiliation":[]},{"given":"Anu","family":"Saini","sequence":"additional","affiliation":[]},{"given":"Sunita","family":"Tiwari","sequence":"additional","affiliation":[]},{"given":"Sunita","family":"Kumari","sequence":"additional","affiliation":[]},{"given":"Syed Noeman","family":"Taqui","sequence":"additional","affiliation":[]},{"given":"Hesham S.","family":"Almoallim","sequence":"additional","affiliation":[]},{"given":"Sulaiman Ali","family":"Alharbi","sequence":"additional","affiliation":[]},{"given":"S. S.","family":"Raghavan","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,27]]},"reference":[{"issue":"3","key":"18705_CR1","first-page":"1","volume":"4","author":"M Mirmozaffari","year":"2019","unstructured":"Mirmozaffari M (2019) Developing an expert system for diagnosing liver diseases. Eur J Eng Technol Res 4(3):1\u20135","journal-title":"Eur J Eng Technol Res"},{"issue":"2","key":"18705_CR2","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.cld.2011.03.006","volume":"15","author":"KA McGlynn","year":"2011","unstructured":"McGlynn KA, London WT (2011) The global epidemiology of hepatocellular carcinoma: present and future. Clin Liver Dis 15(2):223\u2013243","journal-title":"Clin Liver Dis"},{"issue":"4","key":"18705_CR3","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1097\/00000658-198510000-00001","volume":"202","author":"SHUNZABURO Iwatsuki","year":"1985","unstructured":"Iwatsuki SHUNZABURO, Gordon RD, Shaw BW Jr, Starzl TE (1985) Role of liver transplantation in cancer therapy. Ann Surg 202(4):401","journal-title":"Ann Surg"},{"issue":"6","key":"18705_CR4","doi-asserted-by":"publisher","first-page":"1643","DOI":"10.1109\/JBHI.2019.2949837","volume":"24","author":"C Sun","year":"2019","unstructured":"Sun C, Xu A, Liu D, Xiong Z, Zhao F, Ding W (2019) Deep learning-based classification of liver cancer histopathology images using only global labels. IEEE J Biomed Health Inf 24(6):1643\u20131651","journal-title":"IEEE J Biomed Health Inf"},{"issue":"3","key":"18705_CR5","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1109\/4233.735785","volume":"2","author":"AN Esgiar","year":"1998","unstructured":"Esgiar AN, Naguib RN, Sharif BS, Bennett MK, Murray A (1998) Microscopic image analysis for quantitative measurement and feature identification of normal and cancerous colonic mucosa. IEEE Trans Inf Technol Biomed 2(3):197\u2013203","journal-title":"IEEE Trans Inf Technol Biomed"},{"issue":"8","key":"18705_CR6","doi-asserted-by":"publisher","first-page":"2738","DOI":"10.1016\/j.patcog.2015.02.023","volume":"48","author":"C Lu","year":"2015","unstructured":"Lu C, Mandal M (2015) Automated analysis and diagnosis of skin melanoma on whole slide histopathological images. Pattern Recogn 48(8):2738\u20132750","journal-title":"Pattern Recogn"},{"issue":"4","key":"18705_CR7","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TBME.2009.2035102","volume":"57","author":"Y Al-Kofahi","year":"2009","unstructured":"Al-Kofahi Y, Lassoued W, Lee W, Roysam B (2009) Improved automatic detection and segmentation of cell nuclei in histopathology images. IEEE Trans Biomed Eng 57(4):841\u2013852","journal-title":"IEEE Trans Biomed Eng"},{"issue":"4","key":"18705_CR8","first-page":"23","volume":"3","author":"AD Belsare","year":"2012","unstructured":"Belsare AD, Mushrif MM (2012) Histopathological image analysis using image processing techniques: an overview. Signal Image Process 3(4):23","journal-title":"Signal Image Process"},{"issue":"3","key":"18705_CR9","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1038\/nrc3219","volume":"12","author":"KH Chow","year":"2012","unstructured":"Chow KH, Factor RE, Ullman KS (2012) The nuclear envelope environment and its cancer connections. Nat Rev Cancer 12(3):196\u2013209","journal-title":"Nat Rev Cancer"},{"issue":"5","key":"18705_CR10","doi-asserted-by":"publisher","first-page":"1400","DOI":"10.1109\/TBME.2014.2303852","volume":"61","author":"M Veta","year":"2014","unstructured":"Veta M, Pluim JP, Van Diest PJ, Viergever MA (2014) Breast cancer histopathology image analysis: a review. IEEE Trans Biomed Eng 61(5):1400\u20131411","journal-title":"IEEE Trans Biomed Eng"},{"issue":"8","key":"18705_CR11","doi-asserted-by":"publisher","DOI":"10.3390\/electronics10080954","volume":"10","author":"L Hassan","year":"2021","unstructured":"Hassan L, Abdel-Nasser M, Saleh A, Omer A, Puig D (2021) Efficient stain-aware nuclei segmentation deep learning framework for multi-center histopathological images. Electronics 10(8):954","journal-title":"Electronics"},{"issue":"3","key":"18705_CR12","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1111\/pin.13309","volume":"73","author":"A Baidoshvili","year":"2023","unstructured":"Baidoshvili A, Khacheishvili M, van der Laak JA, van Diest PJ (2023) A whole-slide imaging based workflow reduces the reading time of pathologists. Pathol Int 73(3):127\u2013134","journal-title":"Pathol Int"},{"key":"18705_CR13","doi-asserted-by":"publisher","first-page":"2359","DOI":"10.1245\/s10434-019-08190-1","volume":"27","author":"H Liao","year":"2020","unstructured":"Liao H, Xiong T, Peng J, Xu L, Liao M, Zhang Z, Zeng Y (2020) Classification and prognosis prediction from histopathological images of hepatocellular carcinoma by a fully automated pipeline based on machine learning. Ann Surg Oncol 27:2359\u20132369","journal-title":"Ann Surg Oncol"},{"key":"18705_CR14","doi-asserted-by":"crossref","unstructured":"Feng S, Yu X, Liang W, Li X, Zhong W, Hu W, ..., Zhang X (2021) Development of a deep learning model to assist with diagnosis of hepatocellular carcinoma. Front Oncol 11:762733","DOI":"10.3389\/fonc.2021.762733"},{"key":"18705_CR15","doi-asserted-by":"crossref","unstructured":"Wang X, Fang Y, Yang S, Zhu D, Wang M, Zhang J, ..., Han X (2021) A hybrid network for automatic hepatocellular carcinoma segmentation in H&E-stained whole slide images. Med Image Anal 68:101914","DOI":"10.1016\/j.media.2020.101914"},{"key":"18705_CR16","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1007\/s11548-021-02410-4","volume":"16","author":"AA Aatresh","year":"2021","unstructured":"Aatresh AA, Alabhya K, Lal S, Kini J, Saxena PP (2021) LiverNet: efficient and robust deep learning model for automatic diagnosis of sub-types of liver hepatocellular carcinoma cancer from H&E stained liver histopathology images. Int J Comput Assist Radiol Surg 16:1549\u20131563","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"18705_CR17","doi-asserted-by":"crossref","unstructured":"Mahmood T, Owais M, Noh KJ, Yoon HS, Koo JH, Haider A, ... Park KR (2021) Accurate segmentation of nuclear regions with multi-organ histopathology images using artificial intelligence for cancer diagnosis in personalized medicine. J Personalized Med 11(6):515","DOI":"10.3390\/jpm11060515"},{"issue":"3","key":"18705_CR18","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1007\/s42835-023-01654-1","volume":"19","author":"PA Babu","year":"2024","unstructured":"Babu PA, Rai AK, Ramesh JVN, Nithyasri A, Sangeetha S, Kshirsagar PR, Rajendran A, Rajaram A, Dilipkumar S (2024) An explainable deep learning approach for oral cancer detection. J Electr Eng Technol 19(3):1837\u20131848","journal-title":"J Electr Eng Technol"},{"key":"18705_CR19","doi-asserted-by":"crossref","unstructured":"Kalaivani K, Kshirsagarr PR, Sirisha Devi J, Bandela SR, Colak I, Nageswara Rao J, Rajaram A (2023) Prediction of biomedical signals using deep learning techniques.\u00a0J Intell Fuzzy Syst\u00a0(Preprint) 1\u201314","DOI":"10.3233\/JIFS-230399"},{"key":"18705_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.118945","volume":"213","author":"I Ahmad","year":"2023","unstructured":"Ahmad I, Xia Y, Cui H, Islam ZU (2023) DAN-NucNet: a dual attention based framework for nuclei segmentation in cancer histology images under wild clinical conditions. Expert Syst Appl 213:118945","journal-title":"Expert Syst Appl"},{"key":"18705_CR21","doi-asserted-by":"crossref","unstructured":"Zaki G, Gudla PR, Lee K, Kim J, Ozbun L, Shachar S, ..., Pegoraro G (2020) A deep learning pipeline for nucleus segmentation. Cytometry Part A 97(12):1248\u20131264","DOI":"10.1002\/cyto.a.24257"},{"issue":"2","key":"18705_CR22","doi-asserted-by":"publisher","first-page":"6005","DOI":"10.1007\/s11042-023-15348-3","volume":"83","author":"LK Singh","year":"2024","unstructured":"Singh LK, Khanna M, Thawkar S, Singh R (2024) Deep-learning based system for effective and automatic blood vessel segmentation from retinal fundus images. Multimed Tools Appl 83(2):6005\u20136049","journal-title":"Multimed Tools Appl"},{"issue":"6","key":"18705_CR23","doi-asserted-by":"publisher","first-page":"807","DOI":"10.1007\/s12530-022-09426-4","volume":"13","author":"LK Singh","year":"2022","unstructured":"Singh LK, Pooja, Garg H, Khanna M (2022) Deep learning system applicability for rapid glaucoma prediction from fundus images across various data sets. Evol Syst 13(6):807\u2013836","journal-title":"Evol Syst"},{"issue":"25","key":"18705_CR24","doi-asserted-by":"publisher","first-page":"39255","DOI":"10.1007\/s11042-023-14970-5","volume":"82","author":"M Khanna","year":"2023","unstructured":"Khanna M, Singh LK, Thawkar S, Goyal M (2023) Deep learning based computer-aided automatic prediction and grading system for diabetic retinopathy. Multimed Tools Appl 82(25):39255\u201339302","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"18705_CR25","doi-asserted-by":"publisher","first-page":"4465","DOI":"10.1007\/s11042-023-15809-9","volume":"83","author":"M Khanna","year":"2024","unstructured":"Khanna M, Singh LK, Thawkar S, Goyal M (2024) PlaNet: a robust deep convolutional neural network model for plant leaves disease recognition. Multimed Tools Appl 83(2):4465\u20134517","journal-title":"Multimed Tools Appl"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18705-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-024-18705-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-024-18705-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,23]],"date-time":"2025-03-23T00:16:48Z","timestamp":1742689008000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-024-18705-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,27]]},"references-count":25,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["18705"],"URL":"https:\/\/doi.org\/10.1007\/s11042-024-18705-y","relation":{},"ISSN":["1573-7721"],"issn-type":[{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2024,3,27]]},"assertion":[{"value":"16 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 February 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"No participation of humans takes place in this implementation process.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"No violation of Human and Animal Rights is involved.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human and animal rights"}},{"value":"Conflict of Interest is not applicable in this work.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}