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Semantic Difference Guidance for the Uncertain Boundary Segmentation of CT Left Atrial Appendage

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, which is closely relevant to anatomical structures including the left atrium (LA) and the left atrial appendage (LAA). Thus, a thorough understanding of the LA and LAA is essential for the AF treatment. In this paper, we have modeled relative relations between the LA and LAA via deep segmentation networks for the first time, and introduce a new LA & LAA CT dataset. To deal with uncertain boundaries between the LA and LAA, we propose the semantic difference module (SDM) based on diffusion theory to refine features with enhanced boundary information. Besides, disconnections between the LA and LAA are frequently observed in the segmentation results due to uncertain boundaries of the LAA region and CT imaging noise. To address this issue, we devise another connectivity-refined network with the connectivity loss. The loss function exerts a distance regularization on coarse predictions from the first-stage network. Experiments demonstrate that our proposed model can achieve state-of-the-art segmentation performance compared with classic convolutional-neural-networks (CNNs) and recent Transformer-based models on this new dataset. Specifically, SDM can also outperform existing methods on refining uncertain boundaries. Codes are available at https://github.com/AlexYouXin/LA-LAA-segmentation.

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