A deep convolutional neural network-based framework for automatic fetal facial standard plane recognition

Zhen Yu, Ee Leng Tan, Dong Ni, Jing Qin, Siping Chen, Shengli Li, Baiying Lei, Tianfu Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

77 Citations (Scopus)

Abstract

Ultrasound imaging has become a prevalent examination method in prenatal diagnosis. Accurate acquisition of fetal facial standard plane (FFSP) is the most important precondition for subsequent diagnosis and measurement. In the past few years, considerable effort has been devoted to FFSP recognition using various hand-crafted features, but the recognition performance is still unsatisfactory due to the high intraclass variation of FFSPs and the high degree of visual similarity between FFSPs and other non-FFSPs. To improve the recognition performance, we propose a method to automatically recognize FFSP via a deep convolutional neural network (DCNN) architecture. The proposed DCNN consists of 16 convolutional layers with small 3 × 3 size kernels and three fully connected layers. A global average pooling is adopted in the last pooling layer to significantly reduce network parameters, which alleviates the overfitting problems and improves the performance under limited training data. Both the transfer learning strategy and a data augmentation technique tailored for FFSP are implemented to further boost the recognition performance. Extensive experiments demonstrate the advantage of our proposed method over traditional approaches and the effectiveness of DCNN to recognize FFSP for clinical diagnosis.

Original languageEnglish
Pages (from-to)874-885
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Volume22
Issue number3
DOIs
Publication statusPublished - May 2018

Keywords

  • Deep convolutional neural network
  • standard plane recognition
  • transfer learning
  • ultrasound image

ASJC Scopus subject areas

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

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