Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks

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

Research output: Chapter in book / Conference proceedingChapter in an edited book (as author)Academic researchpeer-review

104 Citations (Scopus)

Abstract

Accurate acquisition of fetal ultrasound (US) standard planes is one of the most crucial steps in obstetric diagnosis. The conventional way of standard plane acquisition requires a thorough knowledge of fetal anatomy and intensive manual labors. Hence, automatic approaches are highly demanded in clinical practice. However, automatic detection of standard planes containing key anatomical structures from US videos remains a challenging problem due to the high intra-class variations of standard planes. Unlike previous studies that developed specific methods for different anatomical standard planes respectively, we present a general framework to detect standard planes from US videos automatically. Instead of utilizing hand-crafted visual features, our framework explores spatio-temporal feature learning with a novel knowledge transferred recurrent neural network (T-RNN), which incorporates a deep hierarchical visual feature extractor and a temporal sequence learning model. In order to extract visual features effectively, we propose a joint learning framework with knowledge transfer across multi-tasks to address the insufficiency issue of limited training data. Extensive experiments on different US standard planes with hundreds of videos corroborate that our method can achieve promising results, which outperform state-of-the-art methods.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages507-514
Number of pages8
DOIs
Publication statusPublished - 1 Jan 2015
Externally publishedYes

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9349
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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