mlsanityData to 7 Oct 2026

Hybrid graph convolutional neural networks for landmark-based anatomical segmentation

arXiv 2106.09832 ↗First posted 17 Jun 2021eess.IV cs.CV cs.LGcited by 10

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Abstract (arXiv)

In this work we address the problem of landmark-based segmentation for anatomical structures. We propose HybridGNet, an encoder-decoder neural architecture which combines standard convolutions for image feature encoding, with graph convolutional neural networks to decode plausible representations of anatomical structures. We benchmark the proposed architecture considering other standard landmark and pixel-based models for anatomical segmentation in chest x-ray images, and found that HybridGNet is more robust to image occlusions. We also show that it can be used to construct landmark-based segmentations from pixel level annotations. Our experimental results suggest that HybridGNet produces accurate and anatomically plausible landmark-based segmentations, by naturally incorporating shape constraints within the decoding process via spectral convolutions.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 16 of its 33 references that are in the map. First posted on arXiv 17 Jun 2021.

Builds on

16 of 33 references.

References · 16

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4 more
  1. 2020High-level Prior-based Loss Functions for Medical Image Segmentation: A Surveycited by 28
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 3; 2024: 1; 2025: 1; 2026: 2.

10 papers.

  1. 2023CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray imagescited by 33
  2. 2022Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysiscited by 25
  3. 2023Multi-view Hybrid Graph Convolutional Network for Volume-to-mesh Reconstruction in Cardiovascular MRIcited by 12
  4. 2022DATR: Domain-adaptive transformer for multi-domain landmark detectioncited by 8
  5. 2022Multi-center anatomical segmentation with heterogeneous labels via landmark-based modelscited by 5
  6. 2025CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray imagescited by 2
  7. 2024Graph Convolutional Neural Networks for Automated Echocardiography View Recognition: A Holistic Approachcited by 1
  8. 2026Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervisioncited by 1
  9. 2023Joint Dense-Point Representation for Contour-Aware Graph Segmentationcited by 0
  10. 2026Self-Supervised Temporal Regularization for Landmark-Based Cardiac Segmentation with Automatic AHA Regional Mappingcited by 0