Unsupervised Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation via Semi-supervised Learning and Label Fusion. (arXiv:2201.10647v1 [cs.CV])

Web: http://arxiv.org/abs/2201.10647

Automatic methods to segment the vestibular schwannoma (VS) tumors and the
cochlea from magnetic resonance imaging (MRI) are critical to VS treatment
planning. Although supervised methods have achieved satisfactory performance in
VS segmentation, they require full annotations by experts, which is laborious
and time-consuming. In this work, we aim to tackle the VS and cochlea
segmentation problem in an unsupervised domain adaptation setting. Our proposed
method leverages both the image-level domain alignment to minimize the domain
divergence and semi-supervised training …

arxiv cv domain adaptation fusion learning segmentation semi-supervised learning supervised learning unsupervised