We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does not allow the implementation of a robust DL model that could produce accurate segmentation results on its own. Furthermore, GOCS does not need precise initialisation compared to locally optimal methods on a continuous space (e.g., Mumford-Shah based level set methods); also, GOCS has smaller memory complexity compared to globally optimal inference on a discrete space (e.g., graph cuts). Experimental results show that the proposed method produces the current state-of-the-art mass segmentation (from DCE- MRI), achieving a mean Dice coefficient of 0.77.
Note: We refer to mass and non-mass-like lesions throughout the paper, not just masses.