Towards the automated segmentation of epicardial and mediastinal fats: A multi-manufacturer approach using intersubject registration and random forest
Quick Take
This study presents an automated method for segmenting epicardial and mediastinal fats in CT images, achieving 98.4% accuracy with minimal user intervention. Utilizing intersubject registration and random forest algorithms, the approach enhances clinical decision support systems by providing precise adipose tissue analysis.
Key Points
- Achieved 98.4% mean accuracy for segmenting cardiac adipose tissues.
- Mean true positive rate of 96.2% and Dice similarity index of 96.8%.
- Method promotes minimal user intervention for ease of reproducibility.
- Utilizes intersubject registration and random forest classification algorithms.
- Enhances clinical decision support systems for evaluating cardiac fat.
Article Content
From source RSS / original summaryarXiv:2605. 29217v1 Announce Type: new Abstract: The amount of fat on the surroundings of the heart is correlated to several health risk factors such as carotid stiffness, coronary artery calcification, atrial fibrillation, atherosclerosis, cancer incidence and others. Furthermore, the cardiac fat varies unrelated to the overall fat of the subject, and, therefore, it reinforces the quantitative analysis of these adipose tissues as being essential.
Clinical decision support systems are computer programs capable of evaluating information and providing a corresponding diagnosis or data to complement the physicists' analyses. The aim of this work is to propose a method capable of fully automatically segmenting two types of cardiac adipose tissues that stand apart from each other by the pericardium on CT images obtained by the standard acquisition protocol used for coronary calcium scoring.
Much effort was devoted to promote minimal user intervention and ease of reproducibility. The methodology proposed in this work consists of a registration, which will roughly adjust input images to a standard, an extraction of features related to pixels and their surrounding area and a segmentation step based on data mining classification algorithms that define if an incoming pixel is of a certain type. Experimentations showed that the achieved mean accuracy for the epicardial and mediastinal fats was 98.
4% with a mean true positive rate of 96. 2%. In average, the Dice similarity index was equal to 96. 8%.
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