Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation
Zhenxi Zhang, Fuchen Zheng, Adnan Iltaf, Yifei Han, Zhenyu Cheng, Yue Du, Bin Li, Tianyong Liu
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
BibTeX
@inproceedings{aaai2026_adaptivemorphpat,
title = {Adaptive Morph-Patch Transformer for Aortic Vessel Segmentation},
author = {Zhenxi Zhang and Fuchen Zheng and Adnan Iltaf and Yifei Han and Zhenyu Cheng and Yue Du and Bin Li and Tianyong Liu and Shoujun Zhou},
booktitle = {AAAI 2026},
year = {2026}
}