{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T07:15:31Z","timestamp":1784013331254,"version":"3.55.0"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T00:00:00Z","timestamp":1764979200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T00:00:00Z","timestamp":1768262400000},"content-version":"vor","delay-in-days":38,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62302495"],"award-info":[{"award-number":["62302495"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100015310","name":"Natural Science Foundation of Xinjiang","doi-asserted-by":"publisher","award":["2023D01E15"],"award-info":[{"award-number":["2023D01E15"]}],"id":[{"id":"10.13039\/501100015310","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100018585","name":"Xinjiang Tianchi Doctoral Project","doi-asserted-by":"publisher","award":["E33B9401"],"award-info":[{"award-number":["E33B9401"]}],"id":[{"id":"10.13039\/501100018585","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1007\/s40747-025-02177-0","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T20:08:21Z","timestamp":1765051701000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Organfit: a multi-scale convolutional model with ellipse fitting for organoid identification"],"prefix":"10.1007","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4861-4787","authenticated-orcid":false,"given":"Le","family":"Tong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinran","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Shu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xun","family":"Deng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feng","family":"Tan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zemin","family":"Kuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yu-An","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhuhong","family":"You","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lun","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengwei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,12,6]]},"reference":[{"issue":"6","key":"2177_CR1","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1016\/j.devcel.2016.08.014","volume":"38","author":"K Kretzschmar","year":"2016","unstructured":"Kretzschmar K, Clevers H (2016) Organoids: modeling development and the stem cell niche in a dish. Dev Cell 38(6):590\u2013600","journal-title":"Dev Cell"},{"issue":"5","key":"2177_CR2","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1016\/j.molmed.2017.02.007","volume":"23","author":"D Dutta","year":"2017","unstructured":"Dutta D, Heo I, Clevers H (2017) Disease modeling in stem cell-derived 3d organoid systems. Trends Mol Med 23(5):393\u2013410","journal-title":"Trends Mol Med"},{"issue":"1","key":"2177_CR3","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1016\/j.cell.2017.11.010","volume":"172","author":"N Sachs","year":"2018","unstructured":"Sachs N, De Ligt J, Kopper O, Gogola E, Bounova G, Weeber F, Balgobind AV, Wind K, Gracanin A, Begthel H et al (2018) A living biobank of breast cancer organoids captures disease heterogeneity. Cell 172(1):373\u2013386","journal-title":"Cell"},{"key":"2177_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12915-021-00958-w","volume":"19","author":"L Hof","year":"2021","unstructured":"Hof L, Moreth T, Koch M, Liebisch T, Kurtz M, Tarnick J, Lissek SM, Verstegen MM, Laan LJ, Huch M et al (2021) Long-term live imaging and multiscale analysis identify heterogeneity and core principles of epithelial organoid morphogenesis. BMC Biol 19:1\u201322","journal-title":"BMC Biol"},{"issue":"16","key":"2177_CR5","doi-asserted-by":"publisher","first-page":"5484","DOI":"10.1039\/D1BM00676B","volume":"9","author":"X Chen","year":"2021","unstructured":"Chen X, Wang Y, Zhang X, Liu C (2021) Advances in super-resolution fluorescence microscopy for the study of nano-cell interactions. Biomater Sci 9(16):5484\u20135496","journal-title":"Biomater Sci"},{"issue":"1","key":"2177_CR6","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1038\/s41592-019-0620-7","volume":"17","author":"S Luro","year":"2020","unstructured":"Luro S, Potvin-Trottier L, Okumus B, Paulsson J (2020) Isolating live cells after high-throughput, long-term, time-lapse microscopy. Nat Methods 17(1):93\u2013100","journal-title":"Nat Methods"},{"key":"2177_CR7","doi-asserted-by":"crossref","unstructured":"Sharma V, Rathore A, Vyas G (2016) Detection of sickle cell anaemia and thalassaemia causing abnormalities in thin smear of human blood sample using image processing. In: 2016 International Conference on Inventive Computation Technologies (ICICT), vol. 3, pp. 1\u20135. IEEE","DOI":"10.1109\/INVENTIVE.2016.7830136"},{"issue":"11","key":"2177_CR8","doi-asserted-by":"publisher","first-page":"15259","DOI":"10.1007\/s12652-020-01773-x","volume":"14","author":"K Pasupa","year":"2023","unstructured":"Pasupa K, Vatathanavaro S, Tungjitnob S (2023) Convolutional neural networks based focal loss for class imbalance problem: a case study of canine red blood cells morphology classification. J Ambient Intell Humaniz Comput 14(11):15259\u201315275","journal-title":"J Ambient Intell Humaniz Comput"},{"issue":"1","key":"2177_CR9","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1038\/s41592-018-0261-2","volume":"16","author":"T Falk","year":"2019","unstructured":"Falk T, Mai D, Bensch R, \u00c7i\u00e7ek \u00d6, Abdulkadir A, Marrakchi Y, B\u00f6hm A, Deubner J, J\u00e4ckel Z, Seiwald K et al (2019) U-net: deep learning for cell counting, detection, and morphometry. Nat Methods 16(1):67\u201370","journal-title":"Nat Methods"},{"issue":"11","key":"2177_CR10","doi-asserted-by":"publisher","first-page":"1010584","DOI":"10.1371\/journal.pcbi.1010584","volume":"18","author":"JM Matthews","year":"2022","unstructured":"Matthews JM, Schuster B, Kashaf SS, Liu P, Ben-Yishay R, Ishay-Ronen D, Izumchenko E, Shen L, Weber CR, Bielski M et al (2022) Organoid: a versatile deep learning platform for tracking and analysis of single-organoid dynamics. PLoS Comput Biol 18(11):1010584","journal-title":"PLoS Comput Biol"},{"key":"2177_CR11","doi-asserted-by":"crossref","unstructured":"MacDonald M, Fennel TR, Singanamalli A, Cruz NM, Yousefhussein M, Al-Kofahi Y, Freedman BS (2020) Improved automated segmentation of human kidney organoids using deep convolutional neural networks. In: Medical Imaging 2020: Image Processing, vol. 11313, pp. 832\u2013839. SPIE","DOI":"10.1117\/12.2549830"},{"key":"2177_CR12","unstructured":"Haja A, Brouwer E, Schomaker L (2023) Self-supervised versus supervised training for segmentation of organoid images. arXiv preprint arXiv:2311.11198"},{"issue":"1","key":"2177_CR13","doi-asserted-by":"publisher","first-page":"5319","DOI":"10.1038\/s41598-017-18815-8","volume":"8","author":"MA Borten","year":"2018","unstructured":"Borten MA, Bajikar SS, Sasaki N, Clevers H, Janes KA (2018) Automated brightfield morphometry of 3d organoid populations by organoseg. Sci Rep 8(1):5319","journal-title":"Sci Rep"},{"issue":"1","key":"2177_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s42003-024-05966-4","volume":"7","author":"JW Lefferts","year":"2024","unstructured":"Lefferts JW, Kroes S, Smith MB, Niem\u00f6ller PJ, Nieuwenhuijze ND, Kooten HN, Ent CK, Beekman JM, Beuningen SF (2024) Orgasegment: deep-learning based organoid segmentation to quantify cftr dependent fluid secretion. Commun Biol 7(1):1\u20139","journal-title":"Commun Biol"},{"key":"2177_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2023.100101","volume":"3","author":"A Haja","year":"2023","unstructured":"Haja A, Horcas-Nieto JM, Bakker BM, Schomaker L (2023) Towards automatization of organoid analysis: a deep learning approach to localize and quantify organoid images. Comput Methods Prog Biomed Update 3:100101","journal-title":"Comput Methods Prog Biomed Update"},{"key":"2177_CR16","doi-asserted-by":"publisher","first-page":"1080273","DOI":"10.3389\/fphar.2022.1080273","volume":"13","author":"X Wang","year":"2022","unstructured":"Wang X, Wu C, Zhang S, Yu P, Li L, Guo C, Li R (2022) A novel deep learning segmentation model for organoid-based drug screening. Front Pharmacol 13:1080273","journal-title":"Front Pharmacol"},{"key":"2177_CR17","unstructured":"Naruenatthanaset K, Chalidabhongse TH, Palasuwan D, Anantrasirichai N, Palasuwan A (2020) Red blood cell segmentation with overlapping cell separation and classification on imbalanced dataset. arXiv preprint arXiv:2012.01321"},{"key":"2177_CR18","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1016\/j.neucom.2015.08.006","volume":"173","author":"M Liao","year":"2016","unstructured":"Liao M, Zhao Y-Q, Li X-H, Dai P-S, Xu X-W, Zhang J-K, Zou B-J (2016) Automatic segmentation for cell images based on bottleneck detection and ellipse fitting. Neurocomputing 173:615\u2013622","journal-title":"Neurocomputing"},{"issue":"12","key":"2177_CR19","doi-asserted-by":"publisher","first-page":"5942","DOI":"10.1109\/TIP.2015.2492828","volume":"24","author":"S Zafari","year":"2015","unstructured":"Zafari S, Eerola T, Sampo J, K\u00e4lvi\u00e4inen H, Haario H (2015) Segmentation of overlapping elliptical objects in silhouette images. IEEE Trans Image Process 24(12):5942\u20135952","journal-title":"IEEE Trans Image Process"},{"key":"2177_CR20","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1016\/j.patcog.2017.06.021","volume":"71","author":"W Zhang","year":"2017","unstructured":"Zhang W, Li H (2017) Automated segmentation of overlapped nuclei using concave point detection and segment grouping. Pattern Recogn 71:349\u2013360","journal-title":"Pattern Recogn"},{"key":"2177_CR21","doi-asserted-by":"crossref","unstructured":"Reshma S, Beegum TR (2017) Microscope image processing for tb diagnosis using shape features and ellipse fitting. In: 2017 IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES), pp. 1\u20137. IEEE","DOI":"10.1109\/SPICES.2017.8091342"},{"issue":"11","key":"2177_CR22","doi-asserted-by":"publisher","first-page":"4321","DOI":"10.1109\/TCSVT.2021.3054471","volume":"31","author":"L Sun","year":"2021","unstructured":"Sun L, Chen Z, Wu QJ, Zhao H, He W, Yan X (2021) Ampnet: average-and max-pool networks for salient object detection. IEEE Trans Circ Syst Video Technol 31(11):4321\u20134333","journal-title":"IEEE Trans Circ Syst Video Technol"},{"key":"2177_CR23","doi-asserted-by":"crossref","unstructured":"Li Z, Guan J, Wang H (2022) A novel dual-supervised convolutional network for retinal vessel segmentation. In: 2022 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML), pp. 567\u2013571. IEEE","DOI":"10.1109\/ICICML57342.2022.10009858"},{"issue":"4","key":"2177_CR24","first-page":"537","volume":"8","author":"D Ziou","year":"1998","unstructured":"Ziou D, Tabbone S (1998) Edge detection techniques-an overview. Pattern Recognit Image Anal Adv Math Theory Appl 8(4):537\u2013559","journal-title":"Pattern Recognit Image Anal Adv Math Theory Appl"},{"key":"2177_CR25","doi-asserted-by":"crossref","unstructured":"Zafari S, Eerola T, Sampo J, K\u00e4lvi\u00e4inen H, Haario H (2017) Comparison of concave point detection methods for overlapping convex objects segmentation. In: Image Analysis: 20th Scandinavian Conference, SCIA 2017, Troms\u00f8, Norway, June 12\u201314, 2017, Proceedings, Part II 20, pp. 245\u2013256. Springer","DOI":"10.1007\/978-3-319-59129-2_21"},{"issue":"3","key":"2177_CR26","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1090\/S0002-9947-1959-0110078-1","volume":"93","author":"H Federer","year":"1959","unstructured":"Federer H (1959) Curvature measures. Trans Am Math Soc 93(3):418\u2013491","journal-title":"Trans Am Math Soc"},{"issue":"5","key":"2177_CR27","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1109\/34.765658","volume":"21","author":"A Fitzgibbon","year":"1999","unstructured":"Fitzgibbon A, Pilu M, Fisher RB (1999) Direct least square fitting of ellipses. IEEE Trans Pattern Anal Mach Intell 21(5):476\u2013480","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2177_CR28","doi-asserted-by":"crossref","unstructured":"Kahveci B, Polatli E, Bastanlar Y, Guven S (2024) Organolabeling: quick and accurate annotation tool for organoid images. bioRxiv: 2024\u201304","DOI":"10.1101\/2024.04.16.589852"},{"issue":"24","key":"2177_CR29","doi-asserted-by":"publisher","first-page":"4623","DOI":"10.1039\/D0LC01010C","volume":"20","author":"L Abdul","year":"2020","unstructured":"Abdul L, Rajasekar S, Lin DS, Raja SV, Sotra A, Feng Y, Liu A, Zhang B (2020) Deep-lumen assay-human lung epithelial spheroid classification from brightfield images using deep learning. Lab Chip 20(24):4623\u20134631","journal-title":"Lab Chip"},{"issue":"1","key":"2177_CR30","doi-asserted-by":"publisher","first-page":"19841","DOI":"10.1038\/s41598-023-46485-2","volume":"13","author":"T Park","year":"2023","unstructured":"Park T, Kim TK, Han YD, Kim K-A, Kim H, Kim HS (2023) Development of a deep learning based image processing tool for enhanced organoid analysis. Sci Rep 13(1):19841","journal-title":"Sci Rep"},{"key":"2177_CR31","unstructured":"Paszke A, Gross S, Chintala S, Chanan G, Yang E, DeVito Z, Lin Z, Desmaison A, Antiga L, Lerer A (2017) Automatic differentiation in pytorch"},{"issue":"12","key":"2177_CR32","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481\u20132495","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2177_CR33","unstructured":"Oktay O, Schlemper J, Folgoc LL, Lee M, Heinrich M, Misawa K, Mori K, McDonagh S, Hammerla NY, Kainz B, et al (2018) Attention u-net: learning where to look for the pancreas. arXiv preprint arXiv:1804.03999"},{"key":"2177_CR34","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","volume":"121","author":"N Ibtehaz","year":"2020","unstructured":"Ibtehaz N, Rahman MS (2020) Multiresunet: rethinking the u-net architecture for multimodal biomedical image segmentation. Neural Netw 121:74\u201387","journal-title":"Neural Netw"},{"issue":"1","key":"2177_CR35","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1109\/TCYB.2022.3162945","volume":"53","author":"G Li","year":"2022","unstructured":"Li G, Liu Z, Zeng D, Lin W, Ling H (2022) Adjacent context coordination network for salient object detection in optical remote sensing images. IEEE Trans Cybern 53(1):526\u2013538","journal-title":"IEEE Trans Cybern"},{"key":"2177_CR36","doi-asserted-by":"crossref","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","DOI":"10.1016\/j.compbiomed.2023.106960"},{"key":"2177_CR37","doi-asserted-by":"crossref","unstructured":"Ibtehaz N, Kihara D (2023) Acc-unet: A completely convolutional unet model for the 2020s. In: International Conference on Medical Image Computing and Computer-assisted Intervention, pp. 692\u2013702. Springer","DOI":"10.1007\/978-3-031-43898-1_66"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02177-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02177-0","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02177-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T08:23:36Z","timestamp":1771230216000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02177-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,6]]},"references-count":37,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2]]}},"alternative-id":["2177"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02177-0","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,6]]},"assertion":[{"value":"3 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 December 2025","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 completing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"67"}}