Uncertainty-based Quality Control for Subcortical Structures Segmentation in T1-weighted Brain MRI

The performance of Deep Learning (DL) models may drastically drop in the presence of characteristics in test images not present in the training set. Then, the automatic detection of these Out-Of-Distribution (OOD) inputs is important to deploy these methods especially for clinical applications. We address this issue in the context of DL-based subcortical structures segmentation on T1w brain MRI. We compare two OOD detection frameworks equipping DL segmentation models, Maximum Softmax Probability and Deterministic Uncertainty Method, and demonstrate the superiority of the latter which allows a robust and versatile identification of artifacts in images.