Standard Plane Localization in Fetal Ultrasound via Domain Transferred Deep Neural Networks

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

Research output: Journal article publicationJournal articleAcademic researchpeer-review

181 Citations (Scopus)

Abstract

Automatic localization of the standard plane containing complicated anatomical structures in ultrasound (US) videos remains a challenging problem. In this paper, we present a learning-based approach to locate the fetal abdominal standard plane (FASP) in US videos by constructing a domain transferred deep convolutional neural network (CNN). Compared with previous works based on low-level features, our approach is able to represent the complicated appearance of the FASP and hence achieve better classification performance. More importantly, in order to reduce the overfitting problem caused by the small amount of training samples, we propose a transfer learning strategy, which transfers the knowledge in the low layers of a base CNN trained from a large database of natural images to our task-specific CNN. Extensive experiments demonstrate that our approach outperforms the state-of-the-art method for the FASP localization as well as the CNN only trained on the limited US training samples. The proposed approach can be easily extended to other similar medical image computing problems, which often suffer from the insufficient training samples when exploiting the deep CNN to represent high-level features.
Original languageEnglish
Article number7090943
Pages (from-to)1627-1636
Number of pages10
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number5
DOIs
Publication statusPublished - 1 Sep 2015
Externally publishedYes

Keywords

  • convolutional neural network
  • deep learning
  • domain transfer
  • knowledge transfer
  • standard plane
  • Ultrasound

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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