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.