  Accurately identifying white matter tracts in medical images is essential for
various applications, including surgery planning and tract-specific analysis.
Supervised machine learning models have reached state-of-the-art solving this
task automatically. However, these models are primarily trained on healthy
subjects and struggle with strong anatomical aberrations, e.g. caused by brain
tumors. This limitation makes them unsuitable for tasks such as preoperative
planning, wherefore time-consuming and challenging manual delineation of the
target tract is typically employed. We propose semi-automatic entropy-based
active learning for quick and intuitive segmentation of white matter tracts
from whole-brain tractography consisting of millions of streamlines. The method
is evaluated on 21 openly available healthy subjects from the Human Connectome
Project and an internal dataset of ten neurosurgical cases. With only a few
annotations, the proposed approach enables segmenting tracts on tumor cases
comparable to healthy subjects (dice=0.71), while the performance of automatic
methods, like TractSeg dropped substantially (dice=0.34) in comparison to
healthy subjects. The method is implemented as a prototype named atTRACTive in
the freely available software MITK Diffusion. Manual experiments on tumor data
showed higher efficiency due to lower segmentation times compared to
traditional ROI-based segmentation.
