Automated Segmentation of Coronary Arteries using Attention-Gated UNet for Precise Diagnosis

Shabir Hussain; Junaid  Abdul Wahid; Muhammad  Ayoub; Huan Tong; Rukhshanda  Rehman

doi:10.57041/pjosr.v3i2.1040

Authors

Shabir Hussain School of Architecture, Harbin Institute of Technology, Shenzhen, China
Junaid Abdul Wahid School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou,450001, Henan, China
Muhammad Ayoub School of Computer Science and Engineering, Central South University, Changsha, 336017, Hunan, China
Huan Tong School of Architecture, Harbin Institute of Technology, Shenzhen, China
Rukhshanda Rehman Department of Zoology, The Islamia University of Bahawalpur, Bahawalpur 63100, Pakistan

DOI:

https://doi.org/10.57041/pjosr.v3i2.1040

Keywords:

Coronary Artery, Attention Gated UNet, , Two-Stage Segmentation, Healthcare, biomedical

Abstract

Computed Tomography Angiography (CTA) has revolutionized coronary artery disease diagnosis and treatment with its high-resolution and non-invasive advantages. Precision in coronary artery segmentation is critical for accurate diagnosis and effective treatment. In this study, we introduce an innovative two-stage approach utilizing fully convolutional neural networks (CNNs) for coronary artery segmentation. Our model combines coarse segmentation with fine segmentation, significantly enhancing accuracy. This dual-stage strategy outperforms conventional single-stage methods, as validated through empirical evaluations. Our approach demonstrates a substantial performance improvement, with a MEAN Jaccard Similarity of 0.8217 and a MEAN Dice Similarity Coefficient of 0.9005, affirming its potential in advancing medical imaging diagnostics and improving coronary artery segmentation.