{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T13:57:50Z","timestamp":1778767070429,"version":"3.51.4"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:00:00Z","timestamp":1752624000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Medical Image Analysis"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.media.2025.103723","type":"journal-article","created":{"date-parts":[[2025,7,16]],"date-time":"2025-07-16T00:37:17Z","timestamp":1752626237000},"page":"103723","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["ROP lesion segmentation via sequence coding and block balancing"],"prefix":"10.1016","volume":"105","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7618-502X","authenticated-orcid":false,"given":"Xiping","family":"Jia","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3716-8747","authenticated-orcid":false,"given":"Jianying","family":"Qiu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dong","family":"Nie","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-2692-1480","authenticated-orcid":false,"given":"Tian","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"8","key":"10.1016\/j.media.2025.103723_b1","doi-asserted-by":"crossref","first-page":"11441","DOI":"10.1007\/s11042-022-12396-z","article-title":"Deep dive in retinal fundus image segmentation using deep learning for retinopathy of prematurity","volume":"81","author":"Agrawal","year":"2022","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.media.2025.103723_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2023.109839","article-title":"HVDROPDB datasets for research in retinopathy of prematurity","volume":"52","author":"Agrawal","year":"2024","journal-title":"Data Brief"},{"key":"10.1016\/j.media.2025.103723_b3","series-title":"2020 4th International Conference on Intelligent Computing and Control Systems","first-page":"1210","article-title":"An efficient ridge detection method for retinopathy of prematurity severity analysis","author":"Amrutha","year":"2020"},{"issue":"01","key":"10.1016\/j.media.2025.103723_b4","doi-asserted-by":"crossref","first-page":"93","DOI":"10.3414\/ME13-01-0081","article-title":"Analysis of underlying causes of inter-expert disagreement in retinopathy of prematurity diagnosis","volume":"54","author":"Ataer-Cansizoglu","year":"2015","journal-title":"Methods Inf. Med."},{"issue":"12","key":"10.1016\/j.media.2025.103723_b5","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"Segnet: A deep convolutional encoder-decoder architecture for image segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.media.2025.103723_b6","series-title":"Transunet: Transformers make strong encoders for medical image segmentation","author":"Chen","year":"2021"},{"issue":"2","key":"10.1016\/j.media.2025.103723_b7","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s10456-007-9066-0","article-title":"Retinopathy of prematurity","volume":"10","author":"Chen","year":"2007","journal-title":"Angiogenesis"},{"key":"10.1016\/j.media.2025.103723_b8","series-title":"Not color blind: AI predicts racial identity from black and white retinal vessel segmentations","author":"Coyner","year":"2021"},{"key":"10.1016\/j.media.2025.103723_b9","doi-asserted-by":"crossref","unstructured":"Fan, M., Lai, S., Huang, J., Wei, X., Chai, Z., Luo, J., Wei, X., 2021. Rethinking BiSeNet for real-time semantic segmentation. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. pp. 9716\u20139725.","DOI":"10.1109\/CVPR46437.2021.00959"},{"issue":"9","key":"10.1016\/j.media.2025.103723_b10","doi-asserted-by":"crossref","first-page":"2763","DOI":"10.1109\/TMI.2023.3264513","article-title":"H2Former: An efficient hierarchical hybrid transformer for medical image segmentation","volume":"42","author":"He","year":"2023","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103723_b11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"issue":"9902","key":"10.1016\/j.media.2025.103723_b12","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1016\/S0140-6736(13)60178-6","article-title":"Retinopathy of prematurity","volume":"382","author":"Hellstr\u00f6m","year":"2013","journal-title":"Lancet"},{"issue":"1","key":"10.1016\/j.media.2025.103723_b13","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1109\/TMI.2018.2863562","article-title":"Automated analysis for retinopathy of prematurity by deep neural networks","volume":"38","author":"Hu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103723_b14","article-title":"RTNet: Relation transformer network for diabetic retinopathy multi-lesion segmentation","author":"Huang","year":"2022","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.media.2025.103723_b15","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q., 2017. Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4700\u20134708.","DOI":"10.1109\/CVPR.2017.243"},{"issue":"9","key":"10.1016\/j.media.2025.103723_b16","doi-asserted-by":"crossref","first-page":"1444","DOI":"10.3390\/electronics9091444","article-title":"Deep learning models for automated diagnosis of retinopathy of prematurity in preterm infants","volume":"9","author":"Huang","year":"2020","journal-title":"Electronics"},{"key":"10.1016\/j.media.2025.103723_b17","doi-asserted-by":"crossref","unstructured":"Intriago-Pazmi\u00f1o, M., Ibarra-Fiallo, J., Alonso-Calvo, R., Crespo, J., 2019. Segmenting retinal vascular net from retinopathy of prematurity images using convolutional neural network. In: Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems. pp. 1\u20135.","DOI":"10.1145\/3368691.3368711"},{"issue":"5","key":"10.1016\/j.media.2025.103723_b18","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.survophthal.2018.04.002","article-title":"Retinopathy of prematurity: a review of risk factors and their clinical significance","volume":"63","author":"Kim","year":"2018","journal-title":"Surv. Ophthalmol."},{"key":"10.1016\/j.media.2025.103723_b19","series-title":"2018 International CET Conference on Control, Communication, and Computing (IC4)","first-page":"171","article-title":"Computer aided detection of demarcation line\/ridges in the retinal fundus images of preterm infants","author":"Kurup","year":"2018"},{"issue":"30","key":"10.1016\/j.media.2025.103723_b20","doi-asserted-by":"crossref","first-page":"36341","DOI":"10.1007\/s11042-021-11208-0","article-title":"Automated detection of retinopathy of prematurity by deep attention network","volume":"80","author":"Lei","year":"2021","journal-title":"Multimedia Tools Appl."},{"key":"10.1016\/j.media.2025.103723_b21","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inform. Sci."},{"key":"10.1016\/j.media.2025.103723_b22","series-title":"2020 25th International Conference on Pattern Recognition","first-page":"7293","article-title":"Deep multiple instance learning with spatial attention for ROP case classification, instance selection and abnormality localization","author":"Li","year":"2021"},{"key":"10.1016\/j.media.2025.103723_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.109191","article-title":"Automated lesion segmentation in fundus images with many-to-many reassembly of features","volume":"136","author":"Liu","year":"2023","journal-title":"Pattern Recognit."},{"key":"10.1016\/j.media.2025.103723_b24","series-title":"2016 Fourth International Conference on 3D Vision (3DV)","first-page":"565","article-title":"V-net: Fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.media.2025.103723_b25","doi-asserted-by":"crossref","unstructured":"Nie, D., Xue, J., Ren, X., 2020. Bidirectional pyramid networks for semantic segmentation. In: Proceedings of the Asian Conference on Computer Vision.","DOI":"10.1007\/978-3-030-69525-5_39"},{"key":"10.1016\/j.media.2025.103723_b26","series-title":"Attention u-net: Learning where to look for the pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.media.2025.103723_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.media.2019.101570","article-title":"Refuge challenge: A unified framework for evaluating automated methods for glaucoma assessment from fundus photographs","volume":"59","author":"Orlando","year":"2020","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.media.2025.103723_b28","first-page":"2825","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"issue":"8","key":"10.1016\/j.media.2025.103723_b29","doi-asserted-by":"crossref","first-page":"4087","DOI":"10.1364\/BOE.461411","article-title":"ADS-Net: attention-awareness and deep supervision based network for automatic detection of retinopathy of prematurity","volume":"13","author":"Peng","year":"2022","journal-title":"Biomed. Opt. Express"},{"key":"10.1016\/j.media.2025.103723_b30","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.media.2025.103723_b31","doi-asserted-by":"crossref","unstructured":"Shrivastava, A., Gupta, A., Girshick, R., 2016. Training region-based object detectors with online hard example mining. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 761\u2013769.","DOI":"10.1109\/CVPR.2016.89"},{"key":"10.1016\/j.media.2025.103723_b32","series-title":"Very deep convolutional networks for large-scale image recognition","author":"Simonyan","year":"2014"},{"key":"10.1016\/j.media.2025.103723_b33","doi-asserted-by":"crossref","unstructured":"Tian, P., Dy, J., Erdogmus, D., Ostmo, S., Campbell, J.P., Chiang, M.F., Ioannidis, S., 2021. Automated ROP Diagnostic System based on Comparisons and U-Net Segmentation. In: The 14th PErvasive Technologies Related To Assistive Environments Conference. pp. 33\u201336.","DOI":"10.1145\/3453892.3454002"},{"key":"10.1016\/j.media.2025.103723_b34","series-title":"Journal of Physics: Conference Series","article-title":"Automated recognition of retinopathy of prematurity with deep neural networks","volume":"Vol. 1187","author":"Wang","year":"2019"},{"key":"10.1016\/j.media.2025.103723_b35","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1016\/j.ebiom.2018.08.033","article-title":"Automated retinopathy of prematurity screening using deep neural networks","volume":"35","author":"Wang","year":"2018","journal-title":"EBioMedicine"},{"key":"10.1016\/j.media.2025.103723_b36","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S., 2018. Cbam: Convolutional block attention module. In: Proceedings of the European Conference on Computer Vision. ECCV, pp. 3\u201319.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"10.1016\/j.media.2025.103723_b37","doi-asserted-by":"crossref","first-page":"10232","DOI":"10.1109\/ACCESS.2018.2881042","article-title":"Development of an automated screening system for retinopathy of prematurity using a deep neural network for wide-angle retinal images","volume":"7","author":"Zhang","year":"2018","journal-title":"IEEE Access"},{"key":"10.1016\/j.media.2025.103723_b38","first-page":"3","article-title":"Unet++: A nested u-net architecture for medical image segmentation","author":"Zhou","year":"2018"}],"container-title":["Medical Image Analysis"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525002701?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1361841525002701?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,17]],"date-time":"2025-09-17T16:28:04Z","timestamp":1758126484000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1361841525002701"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":38,"alternative-id":["S1361841525002701"],"URL":"https:\/\/doi.org\/10.1016\/j.media.2025.103723","relation":{},"ISSN":["1361-8415"],"issn-type":[{"value":"1361-8415","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ROP lesion segmentation via sequence coding and block balancing","name":"articletitle","label":"Article Title"},{"value":"Medical Image Analysis","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.media.2025.103723","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"103723"}}