CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images

Matias Cosarinsky, Nicolás Gaggion, Rodrigo Echeveste, Enzo Ferrante

Keywords: landmark-based anatomical segmentation, uncertainty estimation, graph neural networks, VAE, out-of-distribution detection, chest x-ray

Abstract: In this work, we study uncertainty estimation for anatomical landmark-based segmentation on chest X-rays. Inspired by hybrid neural network architectures that combine standard image convolutional encoders with graph-based generative decoders, and leveraging their variational latent space, we derive two complementary measures: (i) latent uncertainty, captured directly from the learned distribution parameters, and (ii) predictive uncertainty, obtained by generating multiple stochastic output predictions from latent samples. Through controlled corruption experiments we show that both uncertainty measures increase with perturbation severity, reflecting both global and local degradation. We demonstrate that these uncertainty signals can identify unreliable predictions by comparing with manual ground-truth, and support out-of-distribution detection on the CheXmask dataset. More importantly, we release **CheXmask-U** (https://huggingface.co/datasets/mcosarinsky/CheXmask-U), a large scale dataset of 657,566 chest X-ray landmark segmentations with per-node uncertainty estimates, enabling researchers to account for spatial variations in segmentation quality when using these anatomical masks. Our findings establish uncertainty estimation as a promising direction to enhance robustness and safe deployment of landmark-based anatomical segmentation methods in chest X-ray. A fully working interactive demo of the method is available at https://huggingface.co/spaces/matiasky/CheXmask-U and the source code at https://github.com/mcosarinsky/CheXmask-U.

Primary Subject Area: Uncertainty Estimation

Secondary Subject Area: Segmentation

Registration Requirement: Yes

Reproducibility: https://github.com/mcosarinsky/CheXmask-U

Visa & Travel: No

Read CFP & Author Instructions: Yes

Originality Policy: Yes

Single-blind & Not Under Review Elsewhere: Yes

LLM Policy: Yes

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Submission Number: 178