Standard plane localization in ultrasound by radial component

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

13 Citations (Scopus)


The acquisition of standard planes is crucial for medical ultrasound (US) diagnosis. In this paper, we present a hierarchical supervised learning framework for automatically detecting standard plane in consecutive 2D US images. The technique is demonstrated by developing a system that localizes fetal abdominal standard plane (FASP) from US videos. We first propose a novel radial component-based model (RCM) to describe the geometric constrains of key anatomical structures (KAS). In order to enhance the detection accuracy, we further adopt random forests classifier for detection of KAS within the regions constrained by RCM. Finally, a second-level classifier combines the results of component detectors to identify a US image as a "FASP" or a "non FASP". Experimental results show that our method significantly outperforms both the full abdomen and the separate anatomy detection methods without geometric constrains.
Original languageEnglish
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
Number of pages4
ISBN (Electronic)9781467319591
Publication statusPublished - 1 Jan 2014
Externally publishedYes
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Renaissance Beijing Capital Hotel, Beijing, China
Duration: 29 Apr 20142 May 2014


Conference2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014


  • Components
  • Fetal abdomen
  • Machine learning
  • Object detection
  • Standard plane
  • Ultrasound

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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