A Machine Learning-Based Approach for Automatic Coronary Sinus Vein Segmentation and Anatomy Reconstruction
Year: 2023
Type: conference-paper
Abstract: The purpose of this study was to develop an approach for reconstructing the anatomy of coronary sinus veins using the proprietary cardiac computer tomography (CT) protocol.The method used in this study is based on a stateof-the-art neural network (NN) for medical image segmentation called Swin-Unet and the 5-fold cross-validation ensemble.The trained NNs were fuzzed using the mean ensemble and the final quality was estimated.The results of the study indicate that the neural network ensemble achieved a quality of 0.76 in terms of the dice score.Furthermore, the study confirmed the efficiency of the proposed cardiac CT protocol for the reconstruction of CS veins in patients with chronic HF and a reduced LV ejection fraction (LV EF < 40%).
Source: Computing in cardiology
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4390446835