Automatic Cardiac Evaluations Using a Deep Video Object Segmentation Network Publisher



Sirjani N¹ ; Moradi S¹ ; Oghli MG^{1, 2} ; Hosseinsabet A³ ; Alizadehasl A⁴ ; Yadollahi M⁴ ; Shiri I⁵ ; Shabanzadeh A¹ Show Affiliations
Source: Insights into Imaging Published:2022

Abstract

Background: Accurate cardiac volume and function assessment have valuable and significant diagnostic implications for patients suffering from ventricular dysfunction and cardiovascular disease. This study has focused on finding a reliable assistant to help physicians have more reliable and accurate cardiac measurements using a deep neural network. EchoRCNN is a semi-automated neural network for echocardiography sequence segmentation using a combination of mask region-based convolutional neural network image segmentation structure with reference-guided mask propagation video object segmentation network. Results: The proposed method accurately segments the left and right ventricle regions in four-chamber view echocardiography series with a dice similarity coefficient of 94.03% and 94.97%, respectively. Further post-processing procedures on the segmented left and right ventricle regions resulted in a mean absolute error of 3.13% and 2.03% for ejection fraction and fractional area change parameters, respectively. Conclusion: This study has achieved excellent performance on the left and right ventricle segmentation, leading to more accurate estimations of vital cardiac parameters such as ejection fraction and fractional area change parameters in the left and right ventricle functionalities, respectively. The results represent that our method can predict an assured, accurate, and reliable cardiac function diagnosis in clinical screenings. © 2022, The Author(s).