Segmentation of suspicious lesions in dynamic, contrast-enhanced breast MR images

Dynamic contrast enhanced breast MRI (DCE BMRI) is an emerging tool for breast cancer diagnosis. There is a clear clinical demand for computer-aided diagnosis (CADx) tools to support radiologists in the diagnostic reading process of DCE BMRI studies. A crucial step in a CADx system is the segmentation of tumors, which allows for accurate assessment of the 3D lesion size and morphology. In this paper we propose a semi-automatic segmentation procedure for suspicious breast lesions. The proposed methodology consists of four steps: (1) Robust seed point selection. This interaction mode ensures robustness of the segmentation result against variations in seed-point placement. (2) Automatic intensity threshold estimation in the subtraction image. (3)Connected component analysis based on the estimated threshold. (4) A post-processing step that includes non-enhancing portions of the lesion into the segmented area and removes attached vessels. The proposed methodology was applied to DCE BMRI data acquired at different institutions using different protocols.