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