Optimizing CT‐Based Models for Predicting Whole‐Body Fat in Rabbits

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Abstract
This study evaluated three computed tomography (CT)‐based automatic fat segmentation methods and identified the optimal regression model for predicting whole‐body fat in rabbits. CT scans were performed on 21 postmortem companion rabbits to estimate fat percentage (Fat%‐CT) and fat volume (FV‐CT, mm 3 ). Two techniques were used to determine CT number (Hounsfield units [HU]) ranges for fat (HU Ranges 1 and 2). These were applied to four body regions using three voxel counting techniques (one‐, two‐, and three‐dimensional techniques) to quantify Fat%‐CT and FV‐CT. Data from whole‐body chemical carcass analysis (whole‐body Fat% and whole‐body fat in grams) were used as gold standard values. The relationship between the extracted CT data and carcass fat estimates was determined using a Spearman rank correlation. Simple linear regression and assumption tests were performed to identify the best model for predicting whole‐body Fat%, and whole‐body fat (g). There was a high correlation between measured fat estimated by CT and by chemical carcass analysis ( r s > 0.7, p < 0.001) for all but one body region—counting combination. The best model predicting whole‐body Fat% used CT data from left kidney region, HU Range 2, and three‐dimensional counting technique. The optimal model predicting whole‐body fat (g) used CT data from the entire abdominal region, HU Range 1, and a one‐dimensional counting technique. The CT‐based models for estimating whole‐body fat in rabbits are feasible. Model optimization requires appropriate selection of the body region, CT number ranges, and counting techniques.

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