Hierarchical convolutional neural networks for segmentation of breast tumors in MRI with application to radiogenomics
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abstract
Breast tumor segmentation based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging problem and an active area of research. Particular challenges, similarly as in other segmentation problems, include the class-imbalance problem as well as confounding background in DCE-MR images. To address these issues, we propose a mask-guided hierarchical learning (MHL) framework for breast tumor segmentation via fully convolutional networks (FCN). Specifically, we first develop an FCN model to generate a 3D breast mask as the region of interest (ROI) for each image, to remove confounding information from input DCE-MR images. We then design a two-stage FCN model to perform coarse-to-fine segmentation for breast tumors. Particularly, we propose a Dice-Sensitivity-like loss function and a reinforcement sampling strategy to handle the class-imbalance problem. To precisely identify locations of tumors that underwent a biopsy, we further propose an FCN model to detect two landmarks located at two nipples. We finally selected the biopsied tumor based on both identified landmarks and segmentations. We validate our MHL method on 272 patients, achieving a mean Dice similarity coefficient (DSC) of 0.72 which is comparable to mutual DSC between expert radiologists. Using the segmented biopsied tumors, we also demonstrate that the automatically generated masks can be applied to radiogenomics and can identify luminal A subtype from other molecular subtypes with the similar accuracy with the analysis based on semi-manual tumor segmentation.
