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

不確定性估計對於醫學影像分割系統的安全臨床部署至關重要，它能識別不可靠的預測結果並輔助人工審核。儘管現有研究主要聚焦於像素級不確定性，但基於標誌點的分割方法具有內在的拓撲保證性，從不確定性角度卻仍待深入探索。本研究針對胸部X光影像的解剖標誌點分割進行不確定性估計分析。受結合標準影像卷積編碼器與基於圖結構的生成式解碼器的混合神經網絡架構啟發，我們利用其變分潛在空間推導出兩種互補的度量指標：（1）潛在不確定性，直接從學習得到的分佈參數中捕獲；（2）預測不確定性，通過從潛在樣本生成多重隨機輸出預測獲得。通過受控損壞實驗證實，兩種不確定性度量均隨擾動強度增加而上升，能同時反映全局與局部圖像退化。我們通過與人工標註真值對比，證明這些不確定性信號可有效識別不可靠預測，並在CheXmask數據集上實現分佈外檢測。更重要的是，我們發布了包含657,566例胸部X光標誌點分割數據的CheXmask-U大規模數據集（huggingface.co/datasets/mcosarinsky/CheXmask-U），提供每個節點的不確定性估計，使研究人員在使用這些解剖掩膜時能考量分割質量的空間差異。本研究確立了不確定性估計作為提升胸部X光解剖標誌點分割方法魯棒性與安全部署前景的重要方向。該方法的完整交互演示見huggingface.co/spaces/matiasky/CheXmask-U，源代碼公開於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.