A Novel Fusion Network for Morphological Analysis of Common Iliac Artery

Abstract

In endovascular interventional therapy, automatic common iliac artery morphological analysis can help physicians plan surgical procedures and assist in the selection of appropriate stents to improve surgical safety. However, different people have distinct blood vessel shapes, and many patients have severe malformations of iliac artery due to hemangiomas. Besides, the uneven distribution of contrast media makes it difficult to make an accurate morphological analysis of the common iliac artery. In this paper, a novel fusion network, combining CNN and Transformer is proposed to address the above issues. The proposed FTU-Net consists of a parallel encoder and a Cross-Fusion module to capture and fuse global context information and local representation. Besides, a hybrid decoder module is designed to better adapt the fused features. Extensive experiments have demonstrated that our proposed method significantly outperforms the best previously published results for this task and achieves the state-of-the-art results on the common iliac artery dataset built by us and two other public medical image datasets. To the best of our knowledge, this is the first approach capable of common iliac artery segmentation.