Active Learning for Scribble-Based Diffusion MRI Segmentation

Scribbles are a popular form of weak annotation for the segmentation of three-dimensional medical images, but typically require iterative refinement to achieve the desired segmentation map. The complexity of diffusion MRI (dMRI) poses additional challenges. Previous work addressed the high dimensionality of dMRI via unsupervised representation learning, and combined it with a random forest classifier that can be re-trained quickly enough to provide interactive feedback to the human annotator. Our work extends that framework in multiple ways. Our main contribution is to add an active learning component that suggests locations in which additional scribbles should be placed. It relies on uncertainty quantification via test time augmentation (TTA). Second, we observe that TTA increases segmentation accuracy even by itself. Moreover, we demonstrate that anomaly detection via isolation forests effectively suppresses false positives that arise when generalizing from sparse scribbles. Taken together, these contributions substantially improve the accuracy that can be achieved with various annotation budgets.