CheXmask-U：基于解剖标志点的X射线图像分割不确定性量化研究

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

December 11, 2025

作者: Matias Cosarinsky, Nicolas Gaggion, Rodrigo Echeveste, Enzo Ferrante

cs.AI

摘要

不确定性估计对于医学图像分割系统的安全临床部署至关重要，它能够识别不可靠的预测并支持人工监督。尽管现有研究主要集中于像素级不确定性，但基于解剖标志点的分割方法虽具有固有拓扑保证，其不确定性研究仍处于探索不足的状态。本研究针对胸部X光影像的解剖标志点分割开展不确定性估计研究。受结合标准图像卷积编码器与基于图结构的生成式解码器的混合神经网络架构启发，我们利用其变分潜空间推导出两种互补的度量指标：（i）潜空间不确定性，直接从学习得到的分布参数中捕获；（ii）预测不确定性，通过从潜空间样本生成多个随机输出来获得。通过受控数据破坏实验表明，两种不确定性度量均随扰动强度增加而上升，能同步反映全局和局部图像退化。通过与人工标注金标准对比，我们验证了这些不确定性信号可有效识别不可靠预测，并在CheXmask数据集上支持分布外检测。更重要的是，我们发布了CheXmask-U大规模数据集（huggingface.co/datasets/mcosarinsky/CheXmask-U），包含657,566例胸部X光标志点分割结果及每个节点的不确定性估计，使研究人员在使用这些解剖掩模时能考量分割质量的空间差异性。我们的研究确立了不确定性估计作为增强胸部X光解剖标志点分割方法鲁棒性与安全部署的重要方向。该方法的完整交互演示见huggingface.co/spaces/matiasky/CheXmask-U，源代码发布于github.com/mcosarinsky/CheXmask-U。

English

Uncertainty estimation is essential for the safe clinical deployment of medical image segmentation systems, enabling the identification of unreliable predictions and supporting human oversight. While prior work has largely focused on pixel-level uncertainty, landmark-based segmentation offers inherent topological guarantees yet remains underexplored from an uncertainty perspective. 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.