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arXiv · 2203.10726

TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers

Di Liu·Yunhe Gao·Qilong Zhangli·Ligong Han·Xiaoxiao He·Zhaoyang Xia·Song Wen·Qi Chang·Zhennan Yan·Mu Zhou·Dimitris Metaxas
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Abstract

Combining information from multi-view images is crucial to improve the performance and robustness of automated methods for disease diagnosis. However, due to the non-alignment characteristics of multi-view images, building correlation and data fusion across views largely remain an open problem. In this study, we present TransFusion, a Transformer-based architecture to merge divergent multi-view imaging information using convolutional layers and powerful attention mechanisms. In particular, the Divergent Fusion Attention (DiFA) module is proposed for rich cross-view context modeling and semantic dependency mining, addressing the critical issue of capturing long-range correlations between unaligned data from different image views. We further propose the Multi-Scale Attention (MSA) to collect global correspondence of multi-scale feature representations. We evaluate TransFusion on the Multi-Disease, Multi-View \& Multi-Center Right Ventricular Segmentation in Cardiac MRI (M\&Ms-2) challenge cohort. TransFusion demonstrates leading performance against the state-of-the-art methods and opens up new perspectives for multi-view imaging integration towards robust medical image segmentation.

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Di Liu, Yunhe Gao, Qilong Zhangli, Ligong Han, Xiaoxiao He, Zhaoyang Xia, Song Wen, Qi Chang, Zhennan Yan, Mu Zhou, Dimitris Metaxas. 2022-03-21. TransFusion: Multi-view Divergent Fusion for Medical Image Segmentation with Transformers. https://arxiv.org/abs/2203.10726

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