Fast fully automatic heart fat segmentation in computed tomography datasets

Highlights

• Thorough assessment of Floor of Log method when coping with the task of heart diagnosis through CT images.

• Comparison of the proposed method with other current approaches.

• Floor of Log method has outperformed the other models in terms of efficiency and effectiveness criteria.

Abstract

Heart diseases affect a large part of the world's population. Studies have shown that these diseases are related to cardiac fat. Various medical diagnostic aid systems are developed to reduce these diseases. In this context, this paper presents a new approach to the segmentation of cardiac fat from Computed Tomography (CT) images. The study employs a clustering algorithm called Floor of Log (FoL). The advantage of this method is the significant drop in segmentation time. Support Vector Machine was used to learn the best FoL algorithm parameter as well as mathematical morphology techniques for noise removal. The time to segment cardiac fat on a CT is only 2.01 s on average. In contrast, literature works require more than one hour to perform segmentation. Therefore, this job is one of the fastest to segment an exam completely. The value of the Accuracy metric was 93.45% and Specificity of 95.52%. The proposed approach is automatic and requires less computational effort. With these results, the use of this approach for the segmentation of cardiac fat proves to be efficient, besides having good application times. Therefore, it has the potential to be a medical diagnostic aid tool. Consequently, it is possible to help experts achieve faster and more accurate results.

Introduction

Many of the diseases that affect the world's population are linked to the heart. Estimates predict that, by 2030, cardiovascular diseases will account for 23.3 million deaths worldwide (Mathers and Loncar, 2006, Peng et al., 2016). In addition, a 16-year study involving 384,597 patients reported that 38.4% of deaths occurred within 28 days following an important coronary event (Dudas et al., 2011). Another study classifies cardiovascular accidents as the most common cause of natural deaths in Jamaica (Causes, 2002). However, there has been a significant growth in cardiovascular research to improve early diagnosis of heart diseases in recent years.

The heart is the most important component of the cardiovascular system since it is responsible for pumping blood to all organs and tissues of the body. It is located between the lungs, as well as in front of the esophagus and the aorta artery (Ravish et al., 2014, Gaborit et al., 2011). The fatty layer around the outside of the heart is called the Epicardial Adipose Tissue (EAT).

EAT is an uncommon visceral fat deposit that has both anatomic and functional contact with the myocardium and coronary arteries (Human, 2007, Corradi et al., 2004). Regarding the physiological aspects, this tissue has cardioprotective properties. When associated with a pathological condition, this fat can affect the parts in which it has contact (Epicardial, 2011).

Fat deposits in the vicinity of the heart are related to various health risks (Peng et al., 2019), such as: in coronary artery calcification (Hartiala et al., 2015, Gorter et al., 2008, Schlett et al., 2012), in coronary artery disease (née Elss et al., 2019, Lossau et al., 2019), in atrial fibrillation (Mahabadi et al., 2014), in atherosclerosis (Djaberi et al., 2008, Yerramasu et al., 2012, Choi et al., 2013, Zhao et al., 2018), and in carotid rigidity (Brinkley et al., 2011), among others (Raggi, 2013).

Echocardiography (Iacobellis et al., 2003), Magnetic Resonance Imaging (MRI) (Kessels et al., 2006, Xu et al., 2018) and Computed Tomography (CT) (Coppini et al., 2010) are non-invasive methods used by physicians to perform a clinical evaluation of this fat (Sicari et al., 2011, Polonsky et al., 2010, Zappaterreno et al., 2003).

Fig. 1 shows images of the three tests used for the quantification of cardiac fat: Echocardiography, Magnetic Resonance Imaging, and Computed Tomography. Although the images resulting from these exams present high resolution, there are differences and peculiarities that show a preference for each one type of examination or another.

Echocardiograms are widely available in the medical field and they have a low cost. However, it is not possible to determine the volume of fat with this examination (Gastaldelli et al., 2013). Magnetic resonance imaging is the gold standard for fat quantification because it presents high spatial resolution and low interobserver variability (Cristobal-Huerta et al., 2015). However, this exam is expensive, resulting in low availability.

Computed tomography compared to echocardiography and magnetic resonance imaging provides a more accurate assessment of epicardial adipose tissue (Coppini et al., 2010). With a CT examination, it is possible to evaluate the thickness, the area and the volume, besides allowing the evaluation of the coronary arteries in the same exam.

Computed Tomography maps the attenuation coefficient of the X-rays that crosses the body under analysis and, from these data, reconstructs a model of this body that represents the anatomical form. The CT scanner was developed by Godfrey Hounsfield, and it provides images in cross-sections of high quality, it is able to process a very large number of measures with very complex mathematical operations, and furthermore it provides results with great accuracy (Kalender, 2006).

Fig. 2 shows an unconfigured computed tomographic image. In this case, even without contrast, the specialist was able to make an appropriate diagnosis. The pericardium (a) is displayed as a thin line in the anterior region of the heart, (b) represents the epicardial fat, (c) is the adipose tissue, and (d) indicates the muscle and cardiac chambers.

The segmentation of cardiac anatomical structures and their fat is a challenge for the diagnosis of cardiovascular diseases (Zhao et al., 2019). The manual procedure can be laborious. Among the main challenges encountered by experts is the fact that accurate diagnosis requires clinical knowledge, radiology, and pathology training. Therefore, there is a need for multidisciplinary work, introducing complexity in care. In manual procedures, the specialist inserts several points to detect fats and separate them from other structures such as lung, aorta, and bones. The specialist then sets the fat intensity values in the Hounsfield units (Molteni, 2013). It subsequently quantifies the fat volume.

Manual and subjective quantifications often involve significant intra- or inter-observer variances. Human limitations, such as physical tiredness, fatigue, and the repetitiveness of actions, are contributing factors to this. Errors in the fat quantification step can affect the analysis of the specialist, and result in an inaccurate diagnosis. Fat quantification that does not match the patient's reality may change the analysis during clinical routine and result in an erroneous diagnosis.

Therefore, semi-automatic and automatic segmentation methods have been proposed to overcome inaccurate hand-segmentation. Methods based on the manual limitation of the pericardial contour and adaptive thresholds initiated the research on fat segmentation. The need for an expert to operate the system and review the results on each exam makes these solutions unviable for clinical applications. Subsequently, were developed atlas-based methods, registration and classification algorithms, and deep learning. The results showed satisfactory results of cardiac fat segmentation, but the segmentation time and the computational effort required to make it challenging to integrate it into daily use by specialists.

The atlas-based methods of Ding et al. (2014) and Shahzad et al. (2019) considered that heart shape data between the trained and the new exam are analogs to each other. However, this idea may be invalidated if there are anatomical variations between individuals. Cardiac fat segmentation with registration and classification algorithms was developed by Rodrigues et al. (2016) and Rodrigues et al. (2017). However, the time to segment an exam does not meet the needs of medical routines. On average, the data similarity index was 97.6%, but to analyze the method required 1.8 h. The authors themselves claimed that it still needed an adaptation to reduce the time. One year later, the same authors reached a time of 0.9 h (Rodrigues et al., 2017). The segmentation time of a full exam with Commandeur textit et al.'s work (Commandeur et al., 2018) was 26 s. However, the method used by the author was deep learning. Thus, the author himself states that the work needs more samples to ascertain its segmentation more accurately.

When it comes to image processing of the heart, one of the main challenges is the difference in pixel-level between cardiac fat, heart, and background that is not easily distinguished. Over the years, works have turned to use other methods due to the difficulty of overcoming this challenge. However, the work bumped into the restrictions already mentioned. Moreover, another challenge that the approach aims to overcome is to perform segmentation in a short time to be able to integrate it into medical routines.

Given all these limitations previously presented, this work proposed approach is a fast, accurate, and fully automated method for the segmentation of cardiac fat in CT imaging. Firstly, the Floor Log cluster (FoL) algorithm, which, along with machine learning, is capable of segmenting almost all heart fat within seconds. Then Mathematical Morphology and Hole Filling Technique were added to the approach to remove the remaining human body components (noises). As far as we know, this is the fastest method of segmenting heart fat, and it has efficient segmentation and low computational cost.

Among the main contributions delivered by this work, it is possible to mention:

• Optimization of cardiac fat segmentation with the Floor Log Cluster (FoL) which combined with machine learning and morphological image processing is able to segment heart fat in seconds.

• Beyond the accuracy of segmentation, the concerned approach focus on the time of segmentation of the exam. Most works in the literature focus only on precision, making its application unfeasible due to the long-time required.

• A new application area for the clustering algorithm Floor of Log (FoL).

• The approach is automatic, without human interaction or some form of manual boot.

The remainder of this article is organized as follows: Section 2 deals with heart fat and its quantification, in addition to presenting the primary literature studies related to its segmentation; Section 3 provides the details of the methodology and describes the proposed technique. In Section 4 the results obtained are displayed and there is a comparison with the results of previous works; Section 5 presents the conclusions, as well as suggestions for future works.