Computerized Lesion Segmentation on DCE-MRI using Active Contours & Spectral Embedding.

Shannon Agner¹, Jun Xu¹, Sudha Karthigeyan¹, Anant Madabhushi¹

Accurate lesion segmentation is an important component of determining quantitative features for lesions on MRI. In this study, we develop an automated segmentation method for delineating lesions on DCE-MRI using spectral embedding which serves as alternative image representation upon which to perform an active contour lesion segmentation. We demonstrate on a cohort of 50 breast DCE-MRI datasets that the automated spectral embedding based active contour (SEAC) provides lesion segmentations that are more comparable to the manual segmentation performed by a radiologist than the popular automated fuzzy c-means segmentation method. While we demonstrate the use of SEAC with breast DCE-MRI data, SEAC could be easily applied to segmenting structures on other high dimensional, time-series imaging data as well.