Abstract
Segmentation of cardiac structures in magnetic resonance imaging is essential to the diagnosis of many cardiovascular diseases. However, sometimes it is challenging to accurately define the right ventricle (RV) structure due to the complex texture. It requires the collaboration of both short-axis (SA) images and long-axis (LA) images. Current deep learning methods trained with single-view data neglect the spatial relations between SA and LA images and could fail at the basal and apex plane of the RV. In order to properly handle the geometrical relations, we proposed a consistency based co-training method that involves an extra penalty for confusing images at the basal and apex plane. At test phase, the anatomical relations are also used for post-processing which further improves the robustness of segmentation. Besides, we incorporate the transformer network in U-Net architecture to enhance its ability to model long-range dependency. We evaluated the proposed method in the M&Ms-2 challenge and obtained promising performance for the segmentation of both SA and LA images.
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Gao, Z., Zhuang, X. (2022). Consistency Based Co-segmentation for Multi-view Cardiac MRI Using Vision Transformer. In: Puyol Antón, E., et al. Statistical Atlases and Computational Models of the Heart. Multi-Disease, Multi-View, and Multi-Center Right Ventricular Segmentation in Cardiac MRI Challenge. STACOM 2021. Lecture Notes in Computer Science(), vol 13131. Springer, Cham. https://doi.org/10.1007/978-3-030-93722-5_33
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DOI: https://doi.org/10.1007/978-3-030-93722-5_33
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