Dynamic Position Transformation and Boundary Refinement Network for Left Atrial Segmentation

• URL: http://arxiv.org/abs/2407.05505v1
• Date: Sun, 7 Jul 2024 22:09:35 GMT
• Title: Dynamic Position Transformation and Boundary Refinement Network for Left Atrial Segmentation
• Authors: Fangqiang Xu, Wenxuan Tu, Fan Feng, Malitha Gunawardhana, Jiayuan Yang, Yun Gu, Jichao Zhao,
• Abstract summary: Left atrial (LA) segmentation is a crucial technique for irregular heartbeat (i.e., atrial fibrillation) diagnosis. Most current methods for LA segmentation strictly assume that the input data is acquired using object-oriented center cropping. We propose a novel Dynamic Position transformation and Boundary refinement Network (DPBNet) to tackle these issues.
• Score: 17.09918110723713
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Left atrial (LA) segmentation is a crucial technique for irregular heartbeat (i.e., atrial fibrillation) diagnosis. Most current methods for LA segmentation strictly assume that the input data is acquired using object-oriented center cropping, while this assumption may not always hold in practice due to the high cost of manual object annotation. Random cropping is a straightforward data pre-processing approach. However, it 1) introduces significant irregularities and incompleteness in the input data and 2) disrupts the coherence and continuity of object boundary regions. To tackle these issues, we propose a novel Dynamic Position transformation and Boundary refinement Network (DPBNet). The core idea is to dynamically adjust the relative position of irregular targets to construct their contextual relationships and prioritize difficult boundary pixels to enhance foreground-background distinction. Specifically, we design a shuffle-then-reorder attention module to adjust the position of disrupted objects in the latent space using dynamic generation ratios, such that the vital dependencies among these random cropping targets could be well captured and preserved. Moreover, to improve the accuracy of boundary localization, we introduce a dual fine-grained boundary loss with scenario-adaptive weights to handle the ambiguity of the dual boundary at a fine-grained level, promoting the clarity and continuity of the obtained results. Extensive experimental results on benchmark dataset have demonstrated that DPBNet consistently outperforms existing state-of-the-art methods.

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