Liver fibrosis is a chronic disease that must be treated to prevent further complications, including liver cancer. The diagnosis of liver fibrosis in CT imaging can be challenging and is often subject to disagreements between radiologists. The nodularity of the liver surface is a well-known feature of fibrosis, which can be quantified in clinical practice with specialized software applications that rely on semi-automatic delineation of the liver contours. This approach, however, requires a high degree of expertise and is time-consuming. While deep learning methods have recently shown excellent performance for liver segmentation, the predicted contours are typically insufficiently accurate for nodularity quantification. In this work, we propose a local thresholding approach to refine the predictions of a deep network trained to segment the liver in CT images. We show that our refinement method improves the estimation of the liver surface nodularity compared to a baseline deep network, with Spearman’s correlation coefficients of 0.60 and 0.47, respectively. This new estimator predicts advanced fibrosis better than the reference clinical approach, with areas under the curve of 74.6% and 67.9%, respectively.