Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation
This addresses segmentation accuracy for aortic vascular structures in cardiovascular disease diagnosis and treatment, representing an incremental improvement over existing Transformer-based methods.
The paper tackles the problem of suboptimal segmentation accuracy in aortic vascular structures caused by fixed-size rectangular patches in Transformer models, proposing an adaptive Morph Patch Transformer that achieves state-of-the-art performance on three open-source datasets.
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.