Academic Department of Informatics, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil.
Academic Department of Informatics, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil; Graduate Program of Production and Systems Engineering, Universidade Tecnológica Federal do Paraná (UTFPR), Pato Branco, 85503-390, Brazil.
Med Eng Phys. 2024 Feb;124:104104. doi: 10.1016/j.medengphy.2024.104104. Epub 2024 Jan 15.
In recent years, research has highlighted the association between increased adipose tissue surrounding the human heart and elevated susceptibility to cardiovascular diseases such as atrial fibrillation and coronary heart disease. However, the manual segmentation of these fat deposits has not been widely implemented in clinical practice due to the substantial workload it entails for medical professionals and the associated costs. Consequently, the demand for more precise and time-efficient quantitative analysis has driven the emergence of novel computational methods for fat segmentation. This study presents a novel deep learning-based methodology that offers autonomous segmentation and quantification of two distinct types of cardiac fat deposits. The proposed approach leverages the pix2pix network, a generative conditional adversarial network primarily designed for image-to-image translation tasks. By applying this network architecture, we aim to investigate its efficacy in tackling the specific challenge of cardiac fat segmentation, despite not being originally tailored for this purpose. The two types of fat deposits of interest in this study are referred to as epicardial and mediastinal fats, which are spatially separated by the pericardium. The experimental results demonstrated an average accuracy of 99.08% and f1-score 98.73 for the segmentation of the epicardial fat and 97.90% of accuracy and f1-score of 98.40 for the mediastinal fat. These findings represent the high precision and overlap agreement achieved by the proposed methodology. In comparison to existing studies, our approach exhibited superior performance in terms of f1-score and run time, enabling the images to be segmented in real time.
近年来，研究强调了人体心脏周围脂肪组织的增加与心血管疾病（如心房颤动和冠心病）易感性之间的关联。然而，由于医学专业人员需要承担大量的工作量和相关成本，这些脂肪沉积物的手动分割并没有在临床实践中得到广泛应用。因此，对更精确和更高效的定量分析的需求推动了用于脂肪分割的新型计算方法的出现。本研究提出了一种新的基于深度学习的方法，该方法能够自主分割和量化两种不同类型的心脏脂肪沉积。所提出的方法利用 pix2pix 网络，这是一种主要用于图像到图像转换任务的生成条件对抗网络。通过应用这种网络架构，我们旨在研究其在解决心脏脂肪分割这一特定挑战方面的有效性，尽管它最初并不是为此目的而设计的。本研究关注的两种脂肪沉积类型分别为心外膜脂肪和纵隔脂肪，它们被心包分隔开来。实验结果表明，心外膜脂肪的分割平均准确率为 99.08%，f1 得分为 98.73%，纵隔脂肪的分割平均准确率为 97.90%，f1 得分为 98.40%。这些发现代表了所提出的方法所达到的高精度和重叠一致性。与现有研究相比，我们的方法在 f1 得分和运行时间方面表现出优越的性能，能够实时分割图像。
