Automated Visceral and Subcutaneous Fat Segmentation in Computed Tomography

Rui Castro, Inês Sousa, Fábio Nunes, Jennifer Mancio, Ricardo Fontes-Carvalho, Carlos A. Ferreira, João Pedrosa

Abstract: Cardiovascular diseases are the leading causes of death worldwide. While there are a number of cardiovascular risk indicators, recent studies have found a connection between cardiovascular risk and the accumulation and characteristics of visceral adipose tissue in the ventral cavity. The quantification of visceral adipose tissue can be easily performed in computed tomography scans but the manual delineation of these structures is a time consuming process subject to variability. This has motivated the development of automatic tools to achieve a faster and more precise solution. This paper explores the use of a U-Net architecture to perform ventral cavity segmentation followed by the use of threshold-based approaches for visceral and subcutaneous adipose tissue segmentation. Experiments with different learning rates, input image sizes and types of loss functions were employed to assess the hyperparameters most suited to this problem. In an external test set, the ventral cavity segmentation model with the best performance achieved a 0.967 Dice Score Coefficient, while the visceral and subcutaneous adipose tissue achieve Dice Score Coefficients of 0.986 and 0.995. Not only are these competitive results when compared to state of the art, the interobserver variability measured in this external dataset was similar to these results confirming the robustness and reliability of the proposed segmentation.