Conventional methods for semi-automatic and rule-based breast segmentation on MR images have a tradeoff between accuracy and segmentation speed. To overcome this, several 2D-deep learning approaches have been proposed, which usually focus on using T1-weighted images to train their models. Our aim is to train a 3D-network for breast segmentation, trained on water-fat MR images, which can be used to measure the breast density based on the proton density fat fraction (PDFF). We show that our model segments both fast and accurately, and can visually outperform our ground truth segmentations while requiring only a few seconds to generate labels.
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