Rodrigues Érick Oliveira, Cordeiro de Morais Felipe Fernandes, Conci Aura
Institute of Computing, Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil.
School of Medicine, Universidade Federal do Rio de Janeiro (UFRJ), Rio de Janeiro, RJ, Brazil.
Stud Health Technol Inform. 2015;216:726-30.
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.
对心脏周围脂肪库进行量化是评估与多种疾病相关的健康风险因素的一种准确方法。然而，由于所需的人力工作量，这种评估类型在临床实践中并未得到广泛应用。这项工作提出了一种用于自动分割心脏脂肪垫的新技术。该技术基于将分类算法应用于心脏CT图像的分割。此外，我们广泛评估了几种算法在这项任务上的性能，并讨论了哪种算法提供了更好的预测模型。实验结果表明，心外膜和纵隔脂肪分类的平均准确率为98.4%，平均真阳性率为96.2%。平均而言，关于分割患者和真实情况的骰子相似性指数等于96.8%。因此，到目前为止，我们的技术在心脏脂肪的自动分割方面取得了最准确的结果。
