Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation

TL;DR

This paper introduces the adaptive Morph Patch Transformer (MPT), a novel architecture that dynamically generates morphology-aware patches and uses semantic clustering attention to improve aortic vessel segmentation accuracy.

Contribution

The paper presents a new adaptive patch partitioning strategy and semantic clustering attention mechanism tailored for vascular segmentation, enhancing the preservation of complex vascular structures.

Findings

MPT achieves state-of-the-art segmentation performance on three datasets.

The adaptive patch strategy preserves vascular structure integrity.

Semantic clustering attention improves segmentation of vessels of varying sizes.

Abstract

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.