mlsanityData to 7 Oct 2026

Learning Topological Interactions for Multi-Class Medical Image Segmentation

arXiv 2207.09654 ↗First posted 20 Jul 2022cs.CVcited by 28

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Abstract (arXiv)

Deep learning methods have achieved impressive performance for multi-class medical image segmentation. However, they are limited in their ability to encode topological interactions among different classes (e.g., containment and exclusion). These constraints naturally arise in biomedical images and can be crucial in improving segmentation quality. In this paper, we introduce a novel topological interaction module to encode the topological interactions into a deep neural network. The implementation is completely convolution-based and thus can be very efficient. This empowers us to incorporate the constraints into end-to-end training and enrich the feature representation of neural networks. The efficacy of the proposed method is validated on different types of interactions. We also demonstrate the generalizability of the method on both proprietary and public challenge datasets, in both 2D and 3D settings, as well as across different modalities such as CT and Ultrasound. Code is available at: https://github.com/TopoXLab/TopoInteraction

Key facts. Cited by 28 papers in the map (data through 7 Oct 2026). Builds on 16 of its 56 references that are in the map. First posted on arXiv 20 Jul 2022.

Builds on

16 of 56 references.

References · 16

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  2. 2017Mask R-CNNcited by 7,259
  3. 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
  4. 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
  5. 2017Rethinking Atrous Convolution for Semantic Image Segmentationcited by 2,154
  6. 20163D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotationcited by 1,471
  7. 2014Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFscited by 552
  8. 2020clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentationcited by 286
  9. 2019Topology-Preserving Deep Image Segmentationcited by 198
  10. 2019A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homologycited by 134
  11. 2021Topology-Aware Segmentation Using Discrete Morse Theorycited by 75
  12. 2021Trigger Hunting with a Topological Prior for Trojan Detectioncited by 39
4 more
  1. 2021Structure-Aware Image Segmentation with Homotopy Warpingcited by 24
  2. 2022Learning Probabilistic Topological Representations Using Discrete Morse Theorycited by 20
  3. 2013Higher-order Segmentation via Multicutscited by 7
  4. 2022A Topology-Attention ConvLSTM Network and Its Application to EM Imagescited by 2

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Citing papers per year: 2022: 1; 2023: 9; 2024: 12; 2025: 6.

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  1. 2024Position: Topological Deep Learning is the New Frontier for Relational Learningcited by 65
  2. 2023The TopCoW Challenge -- Topology-Aware Circle of Willis Segmentation for CT and MR Angiographycited by 48
  3. 2023Topology-Aware Uncertainty for Image Segmentationcited by 32
  4. 2024TopoCellGen: Generating Histopathology Cell Topology with a Diffusion Modelcited by 16
  5. 2023Enhancing Modality-Agnostic Representations via Meta-Learning for Brain Tumor Segmentationcited by 14
  6. 2024Topologically Faithful Multi-class Segmentation in Medical Imagescited by 12
  7. 2023Semi-supervised Segmentation of Histopathology Images with Noise-Aware Topological Consistencycited by 11
  8. 2024S-SAM: SVD-based Fine-Tuning of Segment Anything Model for Medical Image Segmentationcited by 11
  9. 2024TopoDiffusionNet: A Topology-aware Diffusion Modelcited by 10
  10. 2023NexToU: Efficient Topology-Aware U-Net for Medical Image Segmentationcited by 9
  11. 2024Efficient Betti Matching Enables Topology-Aware 3D Segmentation via Persistent Homologycited by 7
  12. 2023Probing the Limits and Capabilities of Diffusion Models for the Anatomic Editing of Digital Twinscited by 6
16 more
  1. 2024Spatial Diffusion for Cell Layout Generationcited by 5
  2. 2025BrainMRDiff: A Diffusion Model for Anatomically Consistent Brain MRI Synthesiscited by 4
  3. 2025MATCH: Multi-faceted Adaptive Topo-Consistency for Semi-Supervised Histopathology Segmentationcited by 3
  4. 2022FAS-UNet: A Novel FAS-driven Unet to Learn Variational Image Segmentationcited by 2
  5. 2023Pairwise-Constrained Implicit Functions for 3D Human Heart Modellingcited by 2
  6. 2024A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Controlcited by 2
  7. 2024Preserving Cardiac Integrity: A Topology-Infused Approach to Whole Heart Segmentationcited by 1
  8. 2025Topology-Aware Wavelet Mamba for Airway Structure Segmentation in Postoperative Recurrent Nasopharyngeal Carcinoma CT Scanscited by 1
  9. 2025TopoMortar: A dataset to evaluate image segmentation methods focused on topology accuracycited by 1
  10. 2023Morphology Edge Attention Network and Optimal Geometric Matching Connection model for vascular segmentationcited by 0
  11. 2023PnPNet: Pull-and-Push Networks for Volumetric Segmentation with Boundary Confusioncited by 0
  12. 2024Learning Topological Representations for Deep Image Understandingcited by 0
  13. 2024Scalar Function Topology Divergence: Comparing Topology of 3D Objectscited by 0
  14. 2024Conformable Convolution for Topologically Aware Learning of Complex Anatomical Structurescited by 0
  15. 2025Leveraging Textual Anatomical Knowledge for Class-Imbalanced Semi-Supervised Multi-Organ Segmentationcited by 0
  16. 2025Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentationcited by 0