On the Automated Segmentation of Epicardial and Mediastinal Cardiac Adipose Tissues Using Classification Algorithms.
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.
Institute of Computing, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.
Érick Oliveira
ÉO
Rodrigues
School of Medicine, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil.
Felipe Fernandes
FF
Cordeiro de Morais
Institute of Computing, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.
Aura
A
Conci
D000273:Adipose Tissue / Q000000981:diagnostic imaging*; D000465:Algorithms; D006801:Humans; D000069550:Machine Learning; D008482:Mediastinum / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods; D010496:Pericardium / Q000000981:diagnostic imaging*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014057:Tomography, X-Ray Computed / Q000379:methods*