CARDIAC MRI SEGMENTATION USING DEEP LEARNING

Abstract

Globally, cardiovascular diseases account for a staggering percentage of all fatalities. According to the World Health Organisation (WHO) in 2016, cardiovascular diseases were responsible for the premature passing away of 17.9 million individuals worldwide. Left and right ventricles, myocardium, and left atrium can be distinguished using a variety of imaging techniques, including coronary angiography, echocardiography, computed tomography (CT), and cardiac magnetic resonance imaging (CMRI). CMRI has emerged as a very promising technique, that provides the most detailed cardiac image to a cardiologist, to arrive at a reliable diagnosis. In patients with congenital and acquired cardiac illness, it serves to assess the details of heart's anatomy and physiology. State-of-the-art CMRI provides improved anatomical information of many cardiac regions as compared to other types of imaging modalities in the short-axis plane. Cardiac indexes; such as ventricle volumes, masses, and ejection fraction in both the end-diastolic (ED) and end-systolic (ES) phases, can be determined using segmentation of the left ventricle (LV), right ventricle (RV), and left ventricular myocardium (LVM). As the key imaging indicator, of the variety of cardiovascular illnesses, including atrial fibrillation, and, stroke and diastolic dysfunction, accurate segmentation of the left atrium (LA) is crucial. Due to inter-observer variation, manual cardiac MR imaging segmentation is a tedious and time-consuming task, generally requiring 2 to 4 min per slice, prompting extensive study of semi- and fully automated segmentation algorithms. Deep learning (DL) methods aim to improve information extraction from MR images. These can help cardiologists, in making efficient clinical decisions, by developing intelligent computer-assisted heart assessment algorithms. The research work in this study has been carried out in three distinct phases. First, investigation has been conducted on techniques for data pre-processing in order to obtain ROI (region-of-interest) and improvement in contrast. Second, an innovative and effective technique for enhancing data (data augmentation) has been developed, as fully annotated cardiac MRI datasets are tiny in size. Third, several deep learning algorithms are designed and developed for segmentation of LA from late gadolinium enhancement (LGE)-MRI dataset and LV, RV and LVM segmentation from short-axis cardiac MR images. These methods have been employed to segment the datasets acquired from multiple clinical centres, multiple vendors, heart conditions, and imaging protocols, as well as datasets from a single clinical centre with consistent imaging protocol. The prime objective of this study is to develop universally applicable intelligent approaches that can be uniformly implemented across all clinical centres across the globe. Previous research investigations have specified the difficulties in apical and basal slices segmentation due to unclear boundaries at apex and base of the heart in short-axis cardiac MR imaging. The application of CLAHE (contrast limited adaptive histogram equalization) to apical and basal slices has significantly improved the segmentation capabilities of short-axis cardiac MR imaging in this study. The attention gate is a technique used to remove irrelevant features, which are not related to the current task.