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 datasets (AVT, AortaSeg24 and TBAD) demonstrate that MPT achieves state-of-the-art performance, with improvements in segmenting intricate vascular structures.

Three-dimensional blood vessel segmentation is an important and challenging task that faces two main difficulties: (1) blood vessel structures are small, making them hard to capture by the network, and vessel edges are difficult to segment accurately; (2) false positives are prone to occur due to the presence of artifacts and noise. This paper proposes a novel blood vessel segmentation method called MambaVesselNet. This method is based on a state-space model and employs a selective state-space time series modeling strategy to achieve a larger receptive field. To better capture fine vascular structures and accurately segment edges, this paper introduces an edge enhancement module and a feature selection module. In terms of data preprocessing, nnUNet's preprocessing strategy is adopted to ensure spatial consistency of the input data. Evaluation on three standard vascular segmentation benchmarks shows that MambaVesselNet achieves state-of-the-art performance. Specifically, on cardiovascular and liver vessel datasets, the Dice coefficient is improved by 1.38% and 2.69%, respectively. The contributions of this paper include the proposal of a new module for enhancing blood vessel edge features, the development of a feature selection module with long sequence modeling capability, and the adoption of nnUNet's data preprocessing strategy, setting a new benchmark for blood vessel segmentation technology.