TY - JOUR
T1 - TGDBN
T2 - Topology-guided dual-backbone network for semi-supervised blastocyst segmentation in assisted reproductive technology
AU - Li, Yiming
AU - Wang, Hua
AU - Hu, Jingfei
AU - Zhang, Jicong
N1 - Publisher Copyright:
© 2026, Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License https://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2026
Y1 - 2026
N2 - 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 single-model dual-head architecture: 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 “hard features” (e.g., 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.
AB - 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 single-model dual-head architecture: 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 “hard features” (e.g., 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.
UR - https://www.scopus.com/pages/publications/105046710238
U2 - 10.1371/journal.pone.0355053
DO - 10.1371/journal.pone.0355053
M3 - 文章
C2 - 42560991
AN - SCOPUS:105046710238
SN - 1932-6203
VL - 21
JO - PLOS One
JF - PLOS One
IS - 8
M1 - e0355053
ER -