Standard Plane Localization in Ultrasound by Radial Component Model and Selective Search

Dong Ni, Xin Yang, Xin Chen, Chien Ting Chin, Siping Chen, Pheng Ann Heng, Shengli Li, Jing Qin, Tianfu Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

38 Citations (Scopus)

Abstract

Acquisition of the standard plane is crucial for medical ultrasound diagnosis. However, this process requires substantial experience and a thorough knowledge of human anatomy. Therefore it is very challenging for novices and even time consuming for experienced examiners. We proposed a hierarchical, supervised learning framework for automatically detecting the standard plane from consecutive 2-D ultrasound images. We tested this technique by developing a system that localizes the fetal abdominal standard plane from ultrasound video by detecting three key anatomical structures: the stomach bubble, umbilical vein and spine. We first proposed a novel radial component-based model to describe the geometric constraints of these key anatomical structures. We then introduced a novel selective search method which exploits the vessel probability algorithm to produce probable locations for the spine and umbilical vein. Next, using component classifiers trained by random forests, we detected the key anatomical structures at their probable locations within the regions constrained by the radial component-based model. Finally, a second-level classifier combined the results from the component detection to identify an ultrasound image as either a "fetal abdominal standard plane" or a "non- fetal abdominal standard plane." Experimental results on 223 fetal abdomen videos showed that the detection accuracy of our method was as high as 85.6% and significantly outperformed both the full abdomen and the separate anatomy detection methods without geometric constraints. The experimental results demonstrated that our system shows great promise for application to clinical practice.
Original languageEnglish
Pages (from-to)2728-2742
Number of pages15
JournalUltrasound in Medicine and Biology
Volume40
Issue number11
DOIs
Publication statusPublished - 1 Nov 2014
Externally publishedYes

Keywords

  • Component
  • Fetal abdomen
  • Machine learning
  • Object detection
  • Selective search
  • Standard plane
  • Ultrasound

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Biophysics
  • Acoustics and Ultrasonics

Cite this