# Multi-center anatomical segmentation with heterogeneous labels via landmark-based models

arXiv: https://arxiv.org/abs/2211.07395 · HTML: https://mlsanity.com/paper/2211.07395/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 21 references that are in the map.
- First posted on arXiv 14 Nov 2022.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [Improving anatomical plausibility in medical image segmentation via hybrid graph neural networks: applications to chest x-ray analysis](https://mlsanity.com/paper/2203.10977/) (2022)
- [Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review](https://mlsanity.com/paper/1812.08577/) (2018)
- [Learning joint segmentation of tissues and brain lesions from task-specific hetero-modal domain-shifted datasets](https://mlsanity.com/paper/2009.04009/) (2020)
- [Unsupervised Domain Adaptation via CycleGAN for White Matter Hyperintensity Segmentation in Multicenter MR Images](https://mlsanity.com/paper/2009.04985/) (2020)
- [Test-time image-to-image translation ensembling improves out-of-distribution generalization in histopathology](https://mlsanity.com/paper/2206.09769/) (2022)
- [Hybrid graph convolutional neural networks for landmark-based anatomical segmentation](https://mlsanity.com/paper/2106.09832/) (2021)
- [Weakly-Supervised Learning of Metric Aggregations for Deformable Image Registration](https://mlsanity.com/paper/1809.09004/) (2018)
- [Combining Heterogeneously Labeled Datasets For Training Segmentation Networks](https://mlsanity.com/paper/1807.08935/) (2018)
- [Learning from Partially Overlapping Labels: Image Segmentation under Annotation Shift](https://mlsanity.com/paper/2107.05938/) (2021)

## Built on by (most cited first, 5 of 5)

- [CheXmask: a large-scale dataset of anatomical segmentation masks for multi-center chest x-ray images](https://mlsanity.com/paper/2307.03293/) (2023)
- [CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images](https://mlsanity.com/paper/2512.10715/) (2025)
- [Unsupervised bias discovery in medical image segmentation](https://mlsanity.com/paper/2309.00451/) (2023)
- [Mask-HybridGNet: Graph-based segmentation with emergent anatomical correspondence from pixel-level supervision](https://mlsanity.com/paper/2602.21179/) (2026)
- [Joint Dense-Point Representation for Contour-Aware Graph Segmentation](https://mlsanity.com/paper/2306.12155/) (2023)

## Abstract (arXiv)

Learning anatomical segmentation from heterogeneous labels in multi-center datasets is a common situation encountered in clinical scenarios, where certain anatomical structures are only annotated in images coming from particular medical centers, but not in the full database. Here we first show how state-of-the-art pixel-level segmentation models fail in naively learning this task due to domain memorization issues and conflicting labels. We then propose to adopt HybridGNet, a landmark-based segmentation model which learns the available anatomical structures using graph-based representations. By analyzing the latent space learned by both models, we show that HybridGNet naturally learns more domain-invariant feature representations, and provide empirical evidence in the context of chest X-ray multiclass segmentation. We hope these insights will shed light on the training of deep learning models with heterogeneous labels from public and multi-center datasets.