Test-Retest analysis of atTRACTive with a dissimilarity uncertainty sampling scheme on the Corpus Callosum of mice

Motivation: Accurate tractography-based segmentation of white matter tracts is crucial for tasks such as pre-surgical planning. Fully automated methods are limited to predefined tracts and struggle with anatomical deviations, e.g. caused by tumors. Goal(s): Our goal is to enhance the manual segmentation process through a novel and intuitive approach. Approach: We recently developed atTRACTive, a tool for semi-automatic fiber dissection relying on entropy-based active learning. In this work, we have improved atTRACTive and conducted an initial evaluation of its test-retest reliability in comparison to traditional ROI-based tract segmentation methods. Results: atTRACTive has demonstrated superior test-retest reliability compared to traditional ROI-based segmentation approaches. Impact: The method offers guidance to researchers in the intuitive and efficient segmentation of arbitrary white matter tracts. Instead of drawing challenging-to-reproduce ROIs, users can simply annotate meaningful streamlines, which are then used to train a classifier.