AUTHOR=Wang Yi , Li Shu-Ting , Huang Jing , Lai Qing-Quan , Guo Yi-Fan , Huang Yin-Hui , Li Yuan-Zhe 

TITLE=Cardiac MRI segmentation of the atria based on UU-NET

JOURNAL=Frontiers in Cardiovascular Medicine

VOLUME=Volume 9 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/cardiovascular-medicine/articles/10.3389/fcvm.2022.1011916

DOI=10.3389/fcvm.2022.1011916

ISSN=2297-055X

ABSTRACT=Backgroud and Objective：In today's society, people's work pressure, coupled with irregular diet, lack of exercise and other bad lifestyle, resulting in frequent cardiovascular diseases. Medical imaging has made great progress in modern society, among which the role of MRI in cardiovascular field is self-evident. Based on this research background, how to process cardiac MRI quickly and accurately by computer has been extensively discussed.In recent years, the breakthrough and development of deep learning algorithms have greatly promoted the reform of various research fields, such as automatic and image segmentation. The application of deep learning algorithm to cardiac MRI can assist doctors in diagnosis, treatment plan, calculation of medical parameters, etc, effectively reduce the workload of doctors, has important significance for computer aided diagnosis system. By comparing and analyzing several traditional image segmentation and deep learning image segmentation, this paper proposes the left and right atria segmentation algorithm of cardiac MRI based on UU-NET network.
Results：The segmentation method based on UU-NET network has achieved good results proposed in this paper, compared with the current mainstream image segmentation algorithm results have been improved to a certain extent. Through the analysis of the experimental results,the image segmentation algorithm based on UU-NET network on the data set, its performance in the verification set and online set is higher than other grid models. The DSC in the verification set is 96.7%, and the DSC in the online set is 96.7%, which is nearly one percentage point higher than the deconvolution neural network model. The hausdorff distance（HD） is 1.2mm. Compared with other deep learning models, it is significantly improved (about 3mm error is reduced), and the time is 0.4min.
Conclusion: The segmentation algorithm based on UU-NET improves the segmentation accuracy obviously compared with other segmentation models. Our technique will be able to help diagnose and treat cardiac complications.