We propose a fully automatic segmentation method called nested graph cut to segment images (2D or 3D) that contain multiple objects with a nested structure. Compared to other graph-cut-based methods developed for multiple regions, our method can work well for nested objects without requiring manual selection of initial seeds, even if different objects have similar intensity distributions and some object boundaries are missing. Promising results were obtained for separating the brain ventricles, the head, and the uterus region in the mouse-embryo head images obtained using high-frequency ultrasound imaging. The proposed method achieved mean Dice similarity coefficients of   $0.87 \pm 0.04$    and   $0.89 \pm 0.06$   for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.
['Jen Wei Kuo', 'Jonathan Mamou', 'Orlando Aristizábal', 'Xuan Zhao', 'Jeffrey A. Ketterling', 'Yao Wang']
Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo