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Multi-Scale Deep Information and Adaptive Attention Mechanism Based Coronary Reconstruction of Superior Mesenteric Artery

Authors :
Kun Zhang
Yu Han
Peixia Xu
Meirong Wang
Jushun Yang
Pengcheng Lin
Danny Crookes
Bosheng He
Liang Hua
Source :
IEEE Access, Vol 11, Pp 4042-4056 (2023)
Publication Year :
2023
Publisher :
IEEE, 2023.

Abstract

Vascular images contain a lot of key information, such as length, diameter and distribution. Thus reconstruction of vessels such as the Superior Mesenteric Artery is critical for the diagnosis of some abdominal diseases. However automatic segmentation of abdominal vessels is extremely challenging due to the multi-scale nature of vessels, boundary-blurring, low contrast, artifact disturbance and vascular cracks in Maximum Intensity Projection images. In this work, we propose a dual attention guided method where an adaptive adjustment field is applied to deal with multi-scale vessel information, and a channel feature fusion module is used to refine the extraction of thin vessels, reducing the interference and background noise. In particular, we propose a novel structure that accepts multiple sequential images as input, and successfully introduces spatial-temporal features by contextual information. A further IterUnet step is introduced to connect tiny cracks caused using CT scans. Comparing our proposed model with other state-of-the-art models, our model yields better segmentation and achieves an average F1 metric of 0.812.

Details

Language :
English
ISSN :
21693536
Volume :
11
Database :
Directory of Open Access Journals
Journal :
IEEE Access
Publication Type :
Academic Journal
Accession number :
edsdoj.670c1efda7194ba0a4f2d58999a6dff4
Document Type :
article
Full Text :
https://doi.org/10.1109/ACCESS.2023.3234997