Zhao, Z., Xu, K., Yeo, H. Z., Yang, X., & Guan, C. (2023). MS-MT: Multi-scale Mean Teacher with Contrastive Unpaired Translation for Cross-Modality Vestibular Schwannoma and Cochlea Segmentation. In Lecture Notes in Computer Science (pp. 68–78). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-44153-0_7
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~\emph{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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Publisher Copyright
Funding Info:
There was no specific funding for the research done
Description:
This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-44153-0_7