Deep Learning Approach for Automatic Segmentation and Functional Assessment of LV in Cardiac MRI

Abstract

The early diagnosis of cardiovascular diseases (CVDs) can effectively prevent them from worsening. The source of the disease can be effectively detected through analysis with cardiac magnetic resonance imaging (CMRI). The segmentation of the left ventricle (LV) in CMRI images plays an indispensable role in the diagnosis of CVDs. However, the automated segmentation of LV is a challenging task, as it is confused with neighboring regions in the cardiac MRI. Deep learning models are effective in performing such complex segmentation because of the high performing convolutional neural networks (CNN). However, since segmentation using CNN involves the pixel-level classification of the image, it lacks the contextual information that is highly desirable in analyzing medical images. In this research, we propose a modified U-Net model to accurately segment the LV using context-enabled segmentation. The proposed model achieves the automatic segmentation and quantitative assessment of LV. The proposed model achieves the state-of-the-art accuracy by effectively utilizing various hyperparameters, such as batch size, batch normalization, activation function, loss function and dropout. Our method demonstrated a statistical significance in the endo- and epicardial walls with a dice score of 0.96 and 0.93, respectively, an average perpendicular distance of 1.73 and percentage of good contours of 96.22 were achieved. Furthermore, a high positive correlation of 0.98 between the clinical parameters, such as ejection fraction, end diastolic volume (EDV), end systolic volume (ESV) and gold standard was obtained.