## 1 Introduction  
Cardiovascular diseases (CVDs) are one of the major causes of death in the world, leading to almost a third of all global deaths [1]. There is a large volume of research linking cardiac adipose tissue (body fat) volume and thickness to several CVDs and general CVD risk. Clinically, the most significant cardiac adipose tissues are epicardial adipose tissue (EAT) and pericardial adipose tissue (PAT) [2]. EAT is a fatty tissue located inside the pericardial sac (pericardium), a thin protective layer around the heart. PAT is the union of all the adipose tissue around the pericardium, which includes EAT. EAT and PAT have been shown to be markers of CVDs [3, 4] but are not widely used in clinical practice due to how difficult it is to accurately measure their volume. Hence, computer-assisted segmentation and quantification of EAT and PAT can be used to improve diagnostics and CVD risk assessment. Segmentation refers to manual, semi-automatic, or fully-automatic identification of EAT and PAT regions on medical images, while quantification refers to measuring EAT and PAT's total volume and density around the heart. Therefore, quantification relies on accurate segmentation. In this paper, we review papers that segment whole EAT or PAT volumes from their surrounding tissues, and not papers that aim to segment different regions inside EAT or PAT, such as left atrial EAT vs. left ventricular EAT. While there is evidence that the distribution of EAT around the heart is important [5], segmenting the whole volume is a prerequisite for the analysis of its distribution and is, therefore, the focus of this review. Manual segmentation and quantification of EAT and PAT are tedious and time-consuming processes often prone to inter-observer variability due to their complex distribution around the heart [6]. The development of computer-aided medical software built upon advanced image processing algorithms reduces the load on radiologists and physicians and provides more input into treatment and disease prevention. Specifically, efficient methods to segment epicardial fat from CT scans are exceedingly beneficial when compared to other imaging modalities, namely because of coronary artery calcium (CAC) scoring. CAC scoring is a procedure done by a CT scan of the heart. CT scans obtained for CAC scoring are also appropriate for measuring EAT volume since it covers the heart and neighborhood. Due to how commonly CAC scoring is done, many medical institutions have existing datasets of CAC scoring CT scans [7], and CAC scoring is often performed as part of large clinical trials [8]. The dataset availability makes it easier to perform future studies of EAT, given an accurate and efficient enough method of quantification. Additionally, segmenting EAT from MRI is more difficult than from CT since the pericardium is often blurred due to partial volume effects [9]. The vast majority of existing EAT and PAT segmentation methods focus on quantifying EAT from CT. Segmenting and quantifying EAT is particularly challenging when compared to other cardiovascular segmentation tasks. EAT is distributed unevenly around the heart, giving it a conspicuous shape that is hard to detect automatically. A compounding issue is that epicardial and paracardial adipose tissues have similar intensities and are delineated by the pericardium, which is often too thin to be fully seen in CT or MRI images. Although various segmentation methods for EAT and PAT have been reported, to the best of our knowledge there is no comprehensive review of these methods in the literature. We hope this review will be useful as a guideline and an indicator for further research directions and algorithm development. This work's primary motivation is to provide a comprehensive and critical overview of automatic EAT and PAT segmentation and quantification methods. The objectives of this study are:  
* to a comprehensive and critical overview of the field and thus increase its visibility and enable future research.