Nested graph cut for automatic segmentation of high-frequency ultrasound images of the Mouse Embryo

Jen Wei Kuo, Jonathan Mamou, Orlando Aristizábal, Xuan Zhao, Jeffrey A. Ketterling, Yao Wang

Research output: Contribution to journalArticle

Abstract

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 graphcut- 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.

Original languageEnglish (US)
Article number2477395
Pages (from-to)427-441
Number of pages15
JournalIEEE Transactions on Medical Imaging
Volume35
Issue number2
DOIs
StatePublished - Feb 1 2016

Keywords

  • Graph cut
  • High-frequency ultrasound
  • Mouse embryo
  • Multi-object
  • Nested structure
  • Segmentation

ASJC Scopus subject areas

  • Software
  • Radiological and Ultrasound Technology
  • Computer Science Applications
  • Electrical and Electronic Engineering

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