Nested Graph Cut for Automatic Segmentation of High-Frequency Ultrasound Images of the Mouse Embryo

• Published in: IEEE transactions on medical imaging Vol. 35; no. 2; pp. 427 - 441
• Main Authors: Jen-wei Kuo, Mamou, Jonathan, Aristizabal, Orlando, Xuan Zhao, Ketterling, Jeffrey A., Yao Wang
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2016; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Animals; Automation; Embryo; Embryo, Mammalian - diagnostic imaging; Gestation; Graph cut; Graphs; Head; Head - diagnostic imaging; high-frequency ultrasound; Image Processing, Computer-Assisted - methods; Image segmentation; Manuals; Mice; mouse embryo; multi-object; nested structure; Segmentation; Shape; Three dimensional; Three-dimensional displays; Ultrasonic imaging; Ultrasonography - methods; Ultrasound
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2015.2477395

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 ±0.04 and 0.89 ±0.06 for segmenting BVs and the head, respectively, compared to manual segmentation results by experts on 40 3D images over five gestation stages.