Ultrasound Standard Plane Detection Using a Composite Neural Network Framework

Hao Chen, Lingyun Wu, Qi Dou, Jing Qin, Shengli Li, Jie Zhi Cheng, Dong Ni, Pheng Ann Heng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

91 Citations (Scopus)


Ultrasound (US) imaging is a widely used screening tool for obstetric examination and diagnosis. Accurate acquisition of fetal standard planes with key anatomical structures is very crucial for substantial biometric measurement and diagnosis. However, the standard plane acquisition is a labor-intensive task and requires operator equipped with a thorough knowledge of fetal anatomy. Therefore, automatic approaches are highly demanded in clinical practice to alleviate the workload and boost the examination efficiency. The automatic detection of standard planes from US videos remains a challenging problem due to the high intraclass and low interclass variations of standard planes, and the relatively low image quality. Unlike previous studies which were specifically designed for individual anatomical standard planes, respectively, we present a general framework for the automatic identification of different standard planes from US videos. Distinct from conventional way that devises hand-crafted visual features for detection, our framework explores in- and between-plane feature learning with a novel composite framework of the convolutional and recurrent neural networks. To further address the issue of limited training data, a multitask learning framework is implemented to exploit common knowledge across detection tasks of distinctive standard planes for the augmentation of feature learning. Extensive experiments have been conducted on hundreds of US fetus videos to corroborate the better efficacy of the proposed framework on the difficult standard plane detection problem.
Original languageEnglish
Article number7890445
Pages (from-to)1576-1583
Number of pages8
JournalIEEE Transactions on Cybernetics
Issue number6
Publication statusPublished - 1 Jun 2017


  • Convolutional neural network (CNN)
  • deep learning
  • knowledge transfer
  • recurrent neural network (RNN)
  • standard plane
  • ultrasound (US)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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