Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation. (arXiv:2209.11879v2 [cs.CV] UPDATED)

Automatic segmentation of vestibular schwannoma (VS) and cochlea from
magnetic resonance imaging can facilitate VS treatment planning. Unsupervised
segmentation methods have shown promising results without requiring the
time-consuming and laborious manual labeling process. In this paper, we present
an approach for VS and cochlea segmentation in an unsupervised domain
adaptation setting. Specifically, we first develop a cross-site cross-modality
unpaired image translation strategy to enrich the diversity of the synthesized
data. Then, we devise a rule-based offline augmentation technique to further …

arxiv data diversity segmentation self-training training unsupervised