Echoes of Adiposity: Unveiling Epicardial Fat Segmentation in Biomedical Imaging

Epicardial fat, an emerging biomarker for cardiovascular diseases, holds significance in clinical research. The world’s leading cause of death is cardiovascular diseases (CVDs), which have evolved into deadly illnesses. As per the record from the United Nations, 17.9 million of the world’s population suffered from CVD. Some of the factors like an unhealthy diet and alcoholism are the major cause of CVD. The technique of manually quantifying these tissues is laborious and prone to mistakes. As a result, this work offers a solid methodology that uses machine learning algorithms to precisely segregate and classify epicardial adipose tissue from cardiac medical image data. The process involves preprocessing techniques for noise reduction and feature extraction, followed by the application of segmentation algorithms to delineate the epicardial fat region accurately. Afterward, a range of machine learning models, like as random forests (RFs), support vector machines (SVMs), and convolutional neural networks (CNNs), are employed for classification tasks in order to differentiate between several forms of fat or to forecast the risks associated with cardiovascular disease. The proposed methodology demonstrates promising results, showcasing high accuracy, sensitivity, and specificity in segmenting and classifying epicardial fat and pericardial adipose tissues offering potential implications. It offers a critical comparison of the approaches, highlights current and upcoming problems in the field, and gives a summary of the studies on the clinical relevance of adipose tissues from the heart and pericardium in addition to providing definitions and terminology from the medical literature. A thorough classification of the underlying techniques is carried out, providing an understanding of how they progressed from conventional image processing techniques to more modern deep learning techniques.