Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization

TL;DR

This paper introduces a method to model annotation and prediction uncertainty in landmark localization using anisotropic Gaussian heatmaps, improving accuracy and interpretability in medical imaging tasks.

Contribution

It proposes learning Gaussian parameters for heatmaps to capture annotation ambiguity and prediction uncertainty, enhancing landmark localization performance.

Findings

State-of-the-art localization accuracy achieved.

Gaussian parameters correlate with observer variability.

Uncertainty integration improves abnormality classification.

Abstract

In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotations. To model the annotation ambiguities of the training dataset, we propose to learn anisotropic Gaussian parameters modeling the shape of the target heatmap during optimization. Furthermore, our method models the prediction uncertainty of individual samples by fitting anisotropic Gaussian functions to the predicted heatmaps during inference. Besides state-of-the-art results, our experiments on datasets of hand radiographs and lateral cephalograms also show that Gaussian functions are correlated with both localization accuracy and observer variability. As a final experiment, we show the importance of integrating the uncertainty into decision making by measuring the influence of the predicted location uncertainty on the classification of anatomical abnormalities in lateral cephalograms.