MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation

• URL: http://arxiv.org/abs/2303.15826v1
• Date: Tue, 28 Mar 2023 08:55:00 GMT
• Title: MS-MT: Multi-Scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation
• Authors: Ziyuan Zhao, Kaixin Xu, Huai Zhe Yeo, Xulei Yang, and Cuntai Guan
• Abstract summary: unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance. We propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4%.
• Score: 11.100048696665496
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain shift has been a long-standing issue for medical image segmentation. Recently, unsupervised domain adaptation (UDA) methods have achieved promising cross-modality segmentation performance by distilling knowledge from a label-rich source domain to a target domain without labels. In this work, we propose a multi-scale self-ensembling based UDA framework for automatic segmentation of two key brain structures i.e., Vestibular Schwannoma (VS) and Cochlea on high-resolution T2 images. First, a segmentation-enhanced contrastive unpaired image translation module is designed for image-level domain adaptation from source T1 to target T2. Next, multi-scale deep supervision and consistency regularization are introduced to a mean teacher network for self-ensemble learning to further close the domain gap. Furthermore, self-training and intensity augmentation techniques are utilized to mitigate label scarcity and boost cross-modality segmentation performance. Our method demonstrates promising segmentation performance with a mean Dice score of 83.8% and 81.4% and an average asymmetric surface distance (ASSD) of 0.55 mm and 0.26 mm for the VS and Cochlea, respectively in the validation phase of the crossMoDA 2022 challenge.

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