On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms

• Published in: Studies in health technology and informatics Vol. 216; p. 726
• Main Authors: Rodrigues, Érick Oliveira, Cordeiro de Morais, Felipe Fernandes, Conci, Aura
• Format: Journal Article
• Language: English
• Published: Netherlands 2015
• Subjects: Adipose Tissue - diagnostic imaging; Algorithms; Humans; Machine Learning; Mediastinum - diagnostic imaging; Pattern Recognition, Automated - methods; Pericardium - diagnostic imaging; Radiographic Image Interpretation, Computer-Assisted - methods; Reproducibility of Results; Sensitivity and Specificity; Subtraction Technique; Tomography, X-Ray Computed - methods
• ISSN: 0926-9630
• DOI: 10.3233/978-1-61499-564-7-726

The quantification of fat depots on the surroundings of the heart is an accurate procedure for evaluating health risk factors correlated with several diseases. However, this type of evaluation is not widely employed in clinical practice due to the required human workload. This work proposes a novel technique for the automatic segmentation of cardiac fat pads. The technique is based on applying classification algorithms to the segmentation of cardiac CT images. Furthermore, we extensively evaluate the performance of several algorithms on this task and discuss which provided better predictive models. Experimental results have shown that the mean accuracy for the classification of epicardial and mediastinal fats has been 98.4% with a mean true positive rate of 96.2%. On average, the Dice similarity index, regarding the segmented patients and the ground truth, was equal to 96.8%. Therfore, our technique has achieved the most accurate results for the automatic segmentation of cardiac fats, to date.