Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging and there is no agreed upon standard. This study compares the results of three fully automated segmentation methods for early prediction of pathologic complete response (pCR) following neoadjuvant treatment. We evaluated three different sub-volumes of interest segmented from DCE-MRI: full stack, half stack, and center 5 slices. The differences between methods were assessed and a univariate logistic regression model was implemented to determine predictive performance of each segmentation method.