A transfer learning approach to few-shot segmentation of novel white matter tracts

•We explore knowledge transfer for few-shot segmentation of novel WM tracts.•A model pretrained for segmenting existing WM tracts is given.•We derive a better network initialization for segmenting novel WM tracts.•Data augmentation is further applied with tract-aware image mixing.•Our method was validated on public and private datasets under various settings.