CheXmask-U: Quantifying uncertainty in landmark-based anatomical segmentation for X-ray images
This work addresses the problem of enhancing robustness and safe deployment of landmark-based anatomical segmentation methods in chest X-ray for medical imaging researchers and practitioners, though it is incremental as it builds on existing hybrid neural network architectures.
The authors tackled uncertainty estimation for anatomical landmark-based segmentation on chest X-rays by deriving latent and predictive uncertainty measures, showing they increase with perturbation severity and can identify unreliable predictions, and released a large-scale dataset of 657,566 chest X-ray landmark segmentations with per-node uncertainty estimates.
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 (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 huggingface.co/spaces/matiasky/CheXmask-U and the source code at github.com/mcosarinsky/CheXmask-U.