{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:13:04Z","timestamp":1786119184927,"version":"build-2736575974"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"8","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"High-level Talent Introduction Project of Anhui University of Traditional Chinese Medicine","award":["2024rcyb03"],"award-info":[{"award-number":["2024rcyb03"]}]},{"name":"Opening Foundation of State Key Laboratory of Cognitive Intelligence, iFLYTEK","award":["COGOS2025SC04"],"award-info":[{"award-number":["COGOS2025SC04"]}]},{"DOI":"10.13039\/501100025352","name":"Hefei Innovation Research Institute, Beihang University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100025352","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>\n                    In the field of assisted reproductive technology (ART), accurate segmentation of blastocyst tissues is crucial for evaluating embryo implantation potential. However, existing semi-supervised learning (SSL) methods often suffer from issues such as structural ambiguity, scale confusion, and insufficient utilization of unlabeled data, while the complex nested structure and inherent transparency of blastocyst images further exacerbate these challenges. To address these challenges, this study proposes a Topology-Guided Dual-Backbone Network (TGDBN), a novel semi-supervised segmentation framework featuring a\n                    <jats:italic>single-model dual-head architecture<\/jats:italic>\n                    : one head for pixel-level segmentation and the other for topology-guided structural optimization. Unlike traditional SSL methods that rely solely on feature consistency, TGDBN incorporates two core innovations within its topology-guided head: 1) Decoder Intra-Class Enhancement (DICE) module: Targeting the last three layers of the decoder (where fine-grained structural information is concentrated), this module adaptively strengthens class-specific \u201chard features\u201d (\n                    <jats:italic>e.g.<\/jats:italic>\n                    , low-contrast boundaries between TE and blastocoel) through category-channel mapping and Gaussian weighting; 2) Multi-Scale Feature Aggregation (MSFA) module: This module upsamples DICE-enhanced multi-scale features to the original image resolution, concatenates them to capture hierarchical structural cues, and feeds the aggregated features into a regression sub-layer to generate explicit topology-guided structural guidance. Experimental validation on a public blastocyst dataset demonstrates that TGDBN outperforms 11 state-of-the-art SSL methods. Visualization results confirm that the network effectively mitigates boundary ambiguity and improves intra-class consistency. Further tests on the left atrial (LA) dataset shows that TGDBN exhibits robust generalization capability. These results verify that TGDBN can effectively leverage unlabeled data to enhance blastocyst segmentation accuracy and structural continuity.\n                  <\/jats:p>","DOI":"10.1371\/journal.pone.0355053","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:37:21Z","timestamp":1786037841000},"page":"e0355053","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["TGDBN: Topology-guided dual-backbone network for semi-supervised blastocyst segmentation in assisted reproductive technology"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-9035-1884","authenticated-orcid":true,"given":"Yiming","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingfei","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3325-5371","authenticated-orcid":true,"given":"Jicong","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"pone.0355053.ref001","first-page":"29","volume-title":"The fine structure of the blastocyst: Some comparative studies","author":"AC Enderasn"},{"issue":"6","key":"pone.0355053.ref002","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1093\/biolre\/ioae062","article-title":"Viewing early life without labels: optical approaches for imaging the early embryo\u2020","volume":"110","author":"DJX Chow","year":"2024","journal-title":"Biol Reprod"},{"key":"pone.0355053.ref003","unstructured":"Wang Z. Computer vision guided embryo biopsy. 2015."},{"issue":"10","key":"pone.0355053.ref004","doi-asserted-by":"crossref","first-page":"3926","DOI":"10.1109\/TVCG.2020.2994013","article-title":"Topology Constrained Shape Correspondence","volume":"27","author":"X Li","year":"2021","journal-title":"IEEE Trans Vis Comput Graph"},{"key":"pone.0355053.ref005","first-page":"1886","article-title":"Incorporating User Interaction and Topological Constraints within Contour Completion via Discrete Calculus","volume":"2013","author":"J Xu","year":"2013","journal-title":"Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit"},{"issue":"81","key":"pone.0355053.ref006","doi-asserted-by":"crossref","first-page":"22","DOI":"10.18372\/1990-5548.81.18986","article-title":"Semi-supervised Segmentation of Medical Images","volume":"3","author":"O Chumachenko","year":"2024","journal-title":"Electron Control Syst"},{"key":"pone.0355053.ref007","doi-asserted-by":"crossref","first-page":"1396887","DOI":"10.3389\/fonc.2024.1396887","article-title":"A semi-supervised segmentation method for microscopic hyperspectral pathological images based on multi-consistency learning","volume":"14","author":"J Fang","year":"2024","journal-title":"Front Oncol"},{"issue":"1","key":"pone.0355053.ref008","doi-asserted-by":"crossref","first-page":"2574","DOI":"10.1038\/s41598-023-29665-y","article-title":"Weakly supervised semantic segmentation for MRI: exploring the advantages and disadvantages of class activation maps for biological image segmentation with soft boundaries","volume":"13","author":"S Syed","year":"2023","journal-title":"Sci Rep"},{"key":"pone.0355053.ref009","doi-asserted-by":"crossref","unstructured":"Li Z, Huang Z, Yan H. 3D Cellular Segmentation of Live Embryos via Topologically and Biologically Boundary-aware Semi-supervised Learning. In: 2024 IEEE International Conference on Systems, Man, and Cybernetics (SMC). 2024. p. 3312\u20137.","DOI":"10.1109\/SMC54092.2024.10831421"},{"key":"pone.0355053.ref010","doi-asserted-by":"crossref","unstructured":"Hu X, Zeng D, Xu X, Shi Y. Semi-supervised Contrastive Learning for Label-Efficient Medical Image Segmentation. Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI. Springer International Publishing; 2021. p. 481\u201390.","DOI":"10.1007\/978-3-030-87196-3_45"},{"key":"pone.0355053.ref011","first-page":"76","article-title":"Gabor filter-embedded U-Net with transformer-based encoding for biomedical image segmentation.","volume-title":"Multiscale multimodal medical imaging","author":"AA Reyes"},{"key":"pone.0355053.ref012","article-title":"Semi-supervised learning with deep generative models","volume":"27","author":"DP Kingma","year":"2014","journal-title":"Adv Neural Inform Process Syst"},{"key":"pone.0355053.ref013","doi-asserted-by":"crossref","unstructured":"Li D, Yang J, Kreis K, Torralba A, Fidler S. Semantic Segmentation with Generative Models: Semi-Supervised Learning and Strong Out-of-Domain Generalization. In: 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). p. 8296\u2013307.","DOI":"10.1109\/CVPR46437.2021.00820"},{"key":"pone.0355053.ref014","unstructured":"Rosnati M, Ribeiro FDS, Monteiro M, de Castro DC, Glocker B. Analysing the effectiveness of a generative model for semi-supervised medical image segmentation. arXiv preprint arXiv:221101886. 2022. doi: arXiv:221101886"},{"key":"pone.0355053.ref015","first-page":"140","article-title":"Semi-supervised medical image segmentation using cross-model pseudo-supervision with shape awareness and local context constraints.","volume-title":"Medical Image Computing and Computer Assisted Intervention \u2013 MICCAI","author":"J Liu","year":"2022"},{"issue":"9","key":"pone.0355053.ref016","doi-asserted-by":"crossref","first-page":"6134","DOI":"10.1002\/mp.17242","article-title":"Semi-supervised learning framework with shape encoding for neonatal ventricular segmentation from 3D ultrasound","volume":"51","author":"Z Szentimrey","year":"2024","journal-title":"Med Phys"},{"key":"pone.0355053.ref017","unstructured":"Tarvainen A, Valpola H. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Adv Neural Inform Process Syst. 2017;30."},{"issue":"12","key":"pone.0355053.ref018","doi-asserted-by":"crossref","first-page":"2968","DOI":"10.1109\/TBME.2017.2759665","article-title":"Automatic Identification of Human Blastocyst Components via Texture","volume":"64","author":"P Saeedi","year":"2017","journal-title":"IEEE Trans Biomed Eng"},{"key":"pone.0355053.ref019","doi-asserted-by":"crossref","first-page":"101832","DOI":"10.1016\/j.media.2020.101832","article-title":"A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging","volume":"67","author":"Z Xiong","year":"2021","journal-title":"Med Image Anal"},{"key":"pone.0355053.ref020","first-page":"408","article-title":"Deep Adversarial Networks for Biomedical Image Segmentation Utilizing Unannotated Images.","volume-title":"Medical Image Computing and Computer Assisted Intervention, MICCAI","author":"Y Zhang","year":"2017"},{"key":"pone.0355053.ref021","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/978-3-030-32245-8_67","article-title":"Uncertainty-Aware Self-ensembling Model for Semi-supervised 3D Left Atrium Segmentation.","volume-title":"Medical Image Computing and Computer Assisted Intervention, MICCAI 2019","author":"L Yu","year":"2019"},{"key":"pone.0355053.ref022","doi-asserted-by":"crossref","unstructured":"Vu T-H, Jain H, Bucher M, Cord M, Perez P. ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019. p. 2512\u201321.","DOI":"10.1109\/CVPR.2019.00262"},{"key":"pone.0355053.ref023","first-page":"10890","article-title":"R-drop: Regularized dropout for neural networks","volume":"34","author":"L Wu","year":"2021","journal-title":"Adv Neural Inform Process Syst"},{"issue":"10","key":"pone.0355053.ref024","doi-asserted-by":"crossref","first-page":"8801","DOI":"10.1609\/aaai.v35i10.17066","article-title":"Semi-supervised Medical Image Segmentation through Dual-task Consistency","volume":"35","author":"X Luo","year":"2021","journal-title":"AAAI"},{"key":"pone.0355053.ref025","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.neunet.2021.10.008","article-title":"Interpolation consistency training for semi-supervised learning","volume":"145","author":"V Verma","year":"2022","journal-title":"Neural Netw"},{"key":"pone.0355053.ref026","doi-asserted-by":"crossref","first-page":"102880","DOI":"10.1016\/j.media.2023.102880","article-title":"Ambiguity-selective consistency regularization for mean-teacher semi-supervised medical image segmentation","volume":"88","author":"Z Xu","year":"2023","journal-title":"Med Image Anal"},{"key":"pone.0355053.ref027","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. In: MICCAI; 2015.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"pone.0355053.ref028","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). 2016. p. 565\u201371.","DOI":"10.1109\/3DV.2016.79"},{"key":"pone.0355053.ref029","doi-asserted-by":"crossref","unstructured":"Wang Y, Xiao B, Bi X, Li W, Gao X. MCF: Mutual Correction Framework for Semi-Supervised Medical Image Segmentation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023. p. 15651\u201360.","DOI":"10.1109\/CVPR52729.2023.01502"},{"key":"pone.0355053.ref030","doi-asserted-by":"crossref","unstructured":"Yang L, Qi L, Feng L, Zhang W, Shi Y. Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation. In: 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2023. p. 7236\u201346.","DOI":"10.1109\/CVPR52729.2023.00699"},{"issue":"1","key":"pone.0355053.ref031","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/TMI.2024.3429340","article-title":"Consistency-Guided Differential Decoding for Enhancing Semi-Supervised Medical Image Segmentation","volume":"44","author":"Q Zeng","year":"2025","journal-title":"IEEE Trans Med Imag"}],"container-title":["PLOS One"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0355053","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T17:37:27Z","timestamp":1786037847000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0355053"}},"subtitle":[],"editor":[{"given":"Francesco","family":"Bardozzo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2026,8,6]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8,6]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0355053","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,6]]}}}