Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation

• URL: http://arxiv.org/abs/2209.11879v1
• Date: Fri, 23 Sep 2022 22:26:51 GMT
• Title: Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation
• Authors: Han Liu, Yubo Fan, Benoit M. Dawant
• Abstract summary: We present an approach for VS and cochlea segmentation in an unsupervised domain adaptation setting. 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 minimize the domain gap. Lastly, we adopt a self-configuring segmentation framework empowered by self-training to obtain the final results.
• Score: 7.327638441664658
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 to further minimize the domain gap. Lastly, we adopt a self-configuring segmentation framework empowered by self-training to obtain the final results. On the CrossMoDA 2022 validation leaderboard, our method has achieved competitive VS and cochlea segmentation performance with mean dice scores of 0.8178 $\pm$ 0.0803 and 0.8433 $\pm$ 0.0293, respectively.

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