动态生成血管对齐补丁，提升主动脉分割精度
Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation
- 自适应生成与血管形态对齐的补丁，保持结构完整性
- 在3个公开数据集上达到当前最优性能
- 适合需要精准分割复杂血管结构的研究者
主动脉血管结构的精确分割对心血管疾病诊断与治疗至关重要。传统基于Transformer的模型虽能捕捉长程依赖关系，但其依赖固定矩形补丁的策略常破坏复杂血管结构的完整性，导致分割效果不佳。为此，我们提出自适应形态补丁Transformer（MPT），一种专为主动脉血管分割设计的新架构。MPT引入自适应补丁划分策略，动态生成与复杂血管结构对齐的形态感知补丁，有效保持单个补丁内语义完整性。同时，提出语义聚类注意力（SCA）方法，动态聚合具有相似语义特征的多个补丁的特征，增强模型对不同尺寸血管的分割能力，维护血管结构完整性。在三个开源数据集（AVT、AortaSeg24和TBAD）上的大量实验表明，MPT在分割复杂血管结构方面显著优于现有方法，实现当前最优性能。
原文摘要 · Abstract (English)
Accurate segmentation of aortic vascular structures is critical for diagnosing and treating cardiovascular diseases.Traditional Transformer-based models have shown promise in this domain by capturing long-range dependencies between vascular features. However, their reliance on fixed-size rectangular patches often influences the integrity of complex vascular structures, leading to suboptimal segmentation accuracy. To address this challenge, we propose the adaptive Morph Patch Transformer (MPT), a novel architecture specifically designed for aortic vascular segmentation. Specifically, MPT introduces an adaptive patch partitioning strategy that dynamically generates morphology-aware patches aligned with complex vascular structures. This strategy can preserve semantic integrity of complex vascular structures within individual patches. Moreover, a Semantic Clustering Attention (SCA) method is proposed to dynamically aggregate features from various patches with similar semantic characteristics. This method enhances the model's capability to segment vessels of varying sizes, preserving the integrity of vascular structures. Extensive experiments on three open-source dataset(AVT, AortaSeg24 and TBAD) demonstrate that MPT achieves state-of-the-art performance, with improvements in segmenting intricate vascular structures.
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