Uncertainty-aware U-Net for Medical Landmark Detection
This work addresses uncertainty in medical landmark detection for healthcare applications, but it is incremental as it builds on existing U-Net and heatmap-based approaches.
The paper tackled the problem of anatomical landmark detection by addressing unrealistic assumptions in heatmap-based methods, such as isotropic landmark distributions, and proposed a Pyramid Covariance Predictor to estimate annotation uncertainty, resulting in improved performance.
Heatmap-based methods play an important role in anatomical landmark detection. However, most current heatmap-based methods assume that the distributions of all landmarks are the same and the distribution of each landmark is isotropic, which may not be in line with reality. For example, the landmark on the jaw is more likely to be located along the edge and less likely to be located inside or outside the jaw. Manually annotating tends to follow similar rules, resulting in an anisotropic distribution for annotated landmarks, which represents the uncertainty in the annotation. To estimate the uncertainty, we propose a module named Pyramid Covariance Predictor to predict the covariance matrices of the target Gaussian distributions, which determine the distributions of landmarks and represent the uncertainty of landmark annotation. Specifically, the Pyramid Covariance Predictor utilizes the pyramid features extracted by the encoder of the backbone U-Net and predicts the Cholesky decomposition of the covariance matrix of the landmark location distribution. Experimental results show that the proposed Pyramid Covariance Predictor can accurately predict the distributions and improve the performance of anatomical landmark detection.