Automated selection of standardized planes from ultrasound volume

Authors:
Bahbibi Rahmatullah;Aris Papageorghiou;J. Alison Noble
Affiliations:
Institute of Biomedical Engineering, Dept. of Engineering Science, University of Oxford;Nuffield Dept of Obstetrics and Gynaecology, John Radcliffe Hospital, University of Oxford;Institute of Biomedical Engineering, Dept. of Engineering Science, University of Oxford
Venue:
MLMI'11 Proceedings of the Second international conference on Machine learning in medical imaging
Year:
2011

Citing 4
Cited 0

A decision-theoretic generalization of on-line learning and an application to boosting

Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Pedestrian Detection Using Wavelet Templates

CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Robust Real-Time Face Detection

International Journal of Computer Vision
Database guided detection of anatomical landmark points in 3D images of the heart

ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro

Quantified Score

Hi-index	0.00

Visualization

Abstract

The search for the standardized planes in a 3D ultrasound volume is a hard and time consuming process even for expert physicians. A scheme for finding the standardized planes would be beneficial in advancing the use of volumetric ultrasound for clinical diagnosis. In this paper, we propose a new method to automatically select the standard plane from the fetal ultrasound volume for the application of fetal biometry measurement. To our knowledge, this is the first study in the fetal ultrasound domain. The method is based on the AdaBoost learning algorithm and has been evaluated on a set of 30 volumes. The experimental results are promising with a recall rate of 91.29%. We believe this will increase the accuracy and efficiency in patient monitoring and care management in obstetrics, specifically in detecting growth restricted fetuses.