Automatic Fetal Ultrasound Standard Plane Detection Using Knowledge Transferred Recurrent Neural Networks
Refereed conference paper presented and published in conference proceedings


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AbstractAccurate 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.
All Author(s) ListChen H, Dou Q, Ni D, Cheng JZ, Qin J, Li SL, Heng PA
Name of Conference18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI)
Start Date of Conference05/10/2015
End Date of Conference09/10/2015
Place of ConferenceMunich
Country/Region of ConferenceGermany
Journal nameLecture Notes in Artificial Intelligence
Detailed descriptionorganized by MICCAI society,
Year2015
Month1
Day1
Volume Number9349
PublisherSPRINGER INT PUBLISHING AG
Pages507 - 514
ISBN978-3-319-24552-2
eISBN978-3-319-24553-9
ISSN0302-9743
LanguagesEnglish-United Kingdom
Web of Science Subject CategoriesComputer Science; Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Computer Science, Theory & Methods; Radiology, Nuclear Medicine & Medical Imaging

Last updated on 2020-15-07 at 01:54