The aim of this study was to test the hypothesis that quantitative analysis of transcutaneous (Transc) ultrasound (US) images can predict the liver fat content with similar accuracy and precision as using intraoperative (Intraop) US. The second goal was to investigate if a tissue mimicking phantom (TMP) might be used as reference for automatic gain compensation (AGC) vs. depth instead of using the data of a set of cows without hepatic alterations. A study was performed in post partum dairy cows (N = 151), as an animal model of human nonalcoholic fatty liver disease (NAFLD), to test these hypotheses. Five Transc and five Intraop US liver images were acquired in each animal and a liver biopsy was taken. In liver tissue samples, triacylglycerol (TAG) content was measured by biochemical analysis and hepatic alterations, other than hepatic steatosis, were excluded by clinical examination. Several preprocessing steps were performed before the ultrasound tissue characteristics (UTC) parameters of B-mode images were derived. Stepwise multiple linear regression analysis was performed on a training set (N = 76) and the results were used on the test group (N = 75) to predict the TAG content in the liver. In all cases, the residual attenuation coefficient (ResAtt) was the only selected parameter. Receiver operating characteristics (ROC) analysis was applied to assess the performance and area under the curve (AUC) of predicting TAG and to compare the sensitivity and specificity of the methods used. High ROC values for AUC (95\%), sensitivity (87\%) and specificity (83\%) for both Intraop and Transc applications with control group as well as with phantom-based AGC were obtained. Consequently, it can be concluded that Transc results are equivalent to Intraop results. Furthermore, equivalent ROC values, when using TMP AGC, indicates the potential use of TMP-based corrections instead of normal group-based corrections. The high predictive values indicate that noninvasive quantitative US has a great potential for staging and screening on hepatic steatosis in cows.