Development of automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans

This contribution describes a novel algorithm for the automated quantification of visceral and subcutaneous adipose tissue volumes from abdominal CT scans of patients referred for colorectal resection. Visceral and subcutaneous adipose tissue volumes can accurately be measured with errors of 1.2 and 0.5%, respectively. Also the reproducibility of CT measurements is good; a disadvantage is the amount of radiation. In this study the diagnostic CT scans in the work - up of (colorectal) cancer were used. This implied no extra radiation. For the purpose of segmentation alone, a low dose protocol can be applied. Obesity is a well known risk factor for complications in and after surgery. Body Mass Index (BMI) is a widely accepted indicator of obesity, but it is not specific for risk assessment of colorectal surgery. We report on an automated method to quantify visceral and subcutaneous adipose tissue volumes as a basic step in a clinical research project concerning preoperative risk assessment. The outcomes are to be correlated with the surgery results. The hypothesis is that the balance between visceral and subcutaneous adipose tissue together with the presence of calcifications in the major bloodvessels, is a predictive indicator for post - operatieve complications such as anastomotic leak. We start with four different computer simulated humanoid abdominal volumes with tissue values in the appropriate Hounsfield range at different dose levels. With satisfactory numerical results for this test, we have applied the algorithm on over a 100 patient scans and have compared results with manual segmentations by an expert for a smaller pilot group. The results are within a 5% difference. Compared to other studies reported in the literature, reliable values are obtained for visceral and subcutaneous adipose tissue areas.