Automated Segmentation of Mass Lesions in Mammography: Mask R-CNN Versus Classical Methods, Fuzzy Clustering and MedSAM

Background/Objectives: The automatic and accurate segmentation of mass lesions in mammograms is a critical step in the computer-aided diagnosis workflow. This study evaluates the mask region-based convolutional neural network (Mask R-CNN), a robust deep learning model for instance-level segmentation, highlighting its growing potential in medical imaging.

Methods

Following configuration optimization, the model is applied to mammography images from the CSAW-CC and TOMPEI-CMMDs, which provide masks annotated by breast radiology experts, to be used as a ground truth. To quantify shape and contour discrepancies with respect to the radiologists' annotations, performance is evaluated with both overlap- and distance-based metrics. Furthermore, the model is tested on synthetic mammograms and benchmarked against classical segmentation methods (Otsu thresholding, gradient vector flow active contour, and Chan-Vese segmentation), Fuzzy c-means clustering, and the state-of-the-art Medical Segment Anything (MedSAM) model.