Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Convolutional LSTM Approach for Left Ventricle Segmentation and Estimation of Left Ventricle Ejection Fraction in Echocardiography

Version 1 : Received: 6 March 2022 / Approved: 10 March 2022 / Online: 10 March 2022 (04:19:30 CET)

How to cite: Jeong, J.G.; Kim, D.; Kim, Y.J.; Yoo, K.; Ha, K.; Chung, W.; Kim, K.G. Convolutional LSTM Approach for Left Ventricle Segmentation and Estimation of Left Ventricle Ejection Fraction in Echocardiography. Preprints 2022, 2022030140 (doi: 10.20944/preprints202203.0140.v1). Jeong, J.G.; Kim, D.; Kim, Y.J.; Yoo, K.; Ha, K.; Chung, W.; Kim, K.G. Convolutional LSTM Approach for Left Ventricle Segmentation and Estimation of Left Ventricle Ejection Fraction in Echocardiography. Preprints 2022, 2022030140 (doi: 10.20944/preprints202203.0140.v1).

Abstract

Cardiovascular disease is the leading cause of death worldwide. A key factor in assessing the risk of cardiovascular disease is left ventricular functional evaluation. Left ventricular (LV) systolic function is evaluated by measuring the left ventricular ejection fraction (LVEF) using echocardiography data. Therefore, quick and accurate left ventricle segmentation is important for estimating the LVEF. However, it is difficult to accurately segment the left ventricle due to changes in the shape and area of the left ventricle during cardiac cycles. In this study, we proposed a framework that considers changes in the shape and area of the left ventricle during the cardiac cycle by applying the convolutional long short-term memory (CLSTM) approach. In addition, we evaluated the left ventricular segmentation and multidimensional quantification of the proposed system in comparison to manual and automated segmentation methods. In addition, to assess the validity of CLSTM, the values of multi-dimensional quantification metrics were compared and analyzed using graphs and Bland–Altman plots on a frame-by-frame basis. We demonstrated that the CLSTM method effectively segments the left ventricle by considering the LV activity. In conclusion, we demonstrated that LV segmentation based on our framework may be utilized to accurately estimate LVEF values.

Keywords

Left ventricular ejection fraction; Left ventricle segmentation; Convolutional long short-term memory; Echocardiography

Subject

MATHEMATICS & COMPUTER SCIENCE, Artificial Intelligence & Robotics

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