Estimating left ventricular volume with ROI-based convolutional neural network

Authors : FENG ZHU

Pages : 23-34

View : 7 | Download : 7

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :The volume of the human left ventricular insert ignore into journalissuearticles values(LV); chamber is an important indicator for diagnosing heart disease. Although LV volume can be measured manually with cardiac magnetic resonance imaging insert ignore into journalissuearticles values(MRI);, the process is difficult and time-consuming for experienced cardiologists. This paper presents an end-to-end segmentation-free method that estimates LV volume from MRI images directly. The method initially uses Fourier transform and a regression filter to calculate the region of interest that contains the LV chambers. Then convolutional neural networks are trained to estimate the end-diastolic volume insert ignore into journalissuearticles values(EDV); and end-systolic volume insert ignore into journalissuearticles values(ESV);. The resulting models accurately estimate the EDV and ESV with a mean absolute error of 15.83 and 9.82 mL, respectively, and an ejection fraction with root mean square error of 5.56%. The comparison results show that the direct estimation methods possess attractive advantages over the previous segmentation-based estimation methods.
Keywords : Convolutional neural network, region of interest, left ventricle volume, magnetic resonance imaging, deep learning