ArticleIEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision2022
TA-Net: Topology-Aware Network for Gland Segmentation.
Article in IEEE Winter Conference on Applications of Computer Vision. IEEE Winter Conference on Applications of Computer Vision, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Fully automatic left ventricle segmentation in [Formula: see text]Rb PET/CT Using a semi-supervised nnU-net.EJNMMI research · 2026Article
- Weakly Supervised Teacher-Student Framework with Progressive Pseudo-mask Refinement for Gland Segmentation.Journal of clinical and translational pathology · 2026Article
- Colon cancer survival prediction from gland shapes within histology slides using deep learning.Journal of integrative bioinformatics · 2025Article
- Article
- TOPODIFFUSIONNET: A TOPOLOGY-AWARE DIFFUSION MODEL.... International Conference on Learning Representations · 2025Article
- Cross-Scale Guidance Integration Transformer for Instance Segmentation in Pathology Images.IEEE open journal of engineering in medicine and biology · 2025Article
- Universal Vessel Segmentation for Multi-Modality Retinal Images.IEEE transactions on image processing : a publication of the IEEE Signal Processing Society · 2025Article
- Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistency.Computer vision - ECCV ... : ... European Conference on Computer Vision : proceedings. European Conference on Computer Vision · 2024Article
- A fine pore-preserved deep neural network for porosity analytics of a high burnup U-10Zr metallic fuel.Scientific reports · 2023Article
- SIAN: STYLE-GUIDED INSTANCE-ADAPTIVE NORMALIZATION FOR MULTI-ORGAN HISTOPATHOLOGY IMAGE SYNTHESIS.Proceedings. IEEE International Symposium on Biomedical Imaging · 2023Article
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Authors and funding
3 authors.
Funding
Abstract
Gland segmentation is a critical step to quantitatively assess the morphology of glands in histopathology image analysis. However, it is challenging to separate densely clustered glands accurately. Existing deep learning-based approaches attempted to use contour-based techniques to alleviate this issue but only achieved limited success. To address this challenge, we propose a novel topology-aware network (TA-Net) to accurately separate densely clustered and severely deformed glands. The proposed TA-Net has a multitask learning architecture and enhances the generalization of gland segmentation by learning shared representation from two tasks: instance segmentation and gland topology estimation. The proposed topology loss computes gland topology using gland skeletons and markers. It drives the network to generate segmentation results that comply with the true gland topology. We validate the proposed approach on the GlaS and CRAG datasets using three quantitative metrics, F1-score, object-level Dice coefficient, and object-level Hausdorff distance. Extensive experiments demonstrate that TA-Net achieves state-of-the-art performance on the two datasets. TA-Net outperforms other approaches in the presence of densely clustered glands.
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