Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation. (arXiv:2209.11879v1 [cs.CV])

Automatic segmentation of vestibular schwannoma (VS) and the cochlea from
magnetic resonance imaging (MRI) 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 …

arxiv data diversity segmentation self-training training unsupervised