We present a segmentation approach that combines GrowCut (GC) with cancer-specific multiparametric Gaussian Mixture Model (GCGMM) to produce accurate and reproducible segmentations. We evaluated GCGMM using a retrospectively collected 75 invasive ductal carcinoma with ERPR+ HER2− (n = 15), triple negative (TN) (n = 9), and ER-HER2+ (n = 57) cancers with variable presentation (mass and non-mass enhancement) and background parenchymal enhancement (mild and marked). Expert delineated manual contours were used to assess the segmentation performance using Dice coefficient (DSC), mean surface distance (mSD), Hausdorff distance, and volume ratio (VR). GCGMM segmentations were significantly more accurate than GrowCut (GC) and fuzzy c-means clustering (FCM). Breast cancer is one of the most commonly diagnosed cancers in women and the second most common cause of cancer-related deaths 1 . Although the increasing availability of novel treatment options has helped to improve survival among patients, robust tools are critically needed to effectively monitor treatment response 2 . Miranikova et al. 3 have shown that tumour volumes measured on magnetic resonance imaging (MRI) predict treatment response in neoadjuvant settings. However, accurate and reproducible tumour segmentation is crucial for evaluating breast cancer response to treatments 4 and to improve surgical outcomes 5 . Accurate and reasonably fast segmentation is critical for radiomics analysis 6 which consists of extracting image features from large datasets with the purpose of identifying non-invasive image-based surrogates for diagnosis (differentiating disease aggressiveness) and for predicting treatment response. Radiomics analysis of breast cancers have been used for predicting cancer treatment outcomes 7-9 and for differentiating between breast cancers by molecular subytpe 10-13 or for classifying cancers by their aggressiveness 14,15 . The first and crucial step in extracting the various texture measures is segmentation of the cancer. With the exception of 11,15 , the vast majority of works have employed manual tumour segmentation for radiomics analysis due to the difficultly in ensuring accurate computer segmentations. However, manual delineation is time consuming. Therefore, majority of works 12-14 including ours 10,16 have used manual segmentation of one or a few representative slices. Recently, semi-automatic segmentations including GrowCut (GC) 17 have been reported to produce more reproducible texture features compared with features computed from manually delineated lung tumors 18 , thereby, underscoring the importance and utility of computer-generated segmentations for high-throughput radiomics.