Global assessment of cardiac function using image statistics in MRI

Authors:
Mariam Afshin;Ismail Ben Ayed;Ali Islam;Aashish Goela;Terry M. Peters;Shuo Li
Affiliations:
University of Western Ontario, London, Canada, Robarts Research Institute, London, Canada;GE Healthcare, London, Canada;University of Western Ontario, London, Canada;University of Western Ontario, London, Canada;University of Western Ontario, London, Canada, Robarts Research Institute, London, Canada;GE Healthcare, London, Canada
Venue:
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
Year:
2012

Citing 6
Cited 0

Fundamentals of neural networks: architectures, algorithms, and applications

Fundamentals of neural networks: architectures, algorithms, and applications
Comprehensive Segmentation of Cine Cardiac MR Images

MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
A Statistical Overlap Prior for Variational Image Segmentation

International Journal of Computer Vision
Automatic segmentation of the left ventricle cavity and myocardium in MRI data

Computers in Biology and Medicine
Left Ventricle Segmentation via Graph Cut Distribution Matching

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Image Segmentation Using Active Contours Driven by the Bhattacharyya Gradient Flow

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The cardiac ejection fraction (EF) depends on the volume variation of the left ventricle (LV) cavity during a cardiac cycle, and is an essential measure in the diagnosis of cardiovascular diseases. It is often estimated via manual segmentation of several images in a cardiac sequence, which is prohibitively time consuming, or via automatic segmentation, which is a challenging and computationally expensive task that may result in high estimation errors. In this study, we propose to estimate the EF in real-time directly from image statistics using machine learning technique. From a simple user input in only one image, we build for all the images in a subject dataset (200 images) a statistic based on the Bhattacharyya coefficient of similarity between image distributions. We demonstrate that these statistics are non-linearly related to the LV cavity areas and, therefore, can be used to estimate the EF via an Artificial Neural Network (ANN) directly. A comprehensive evaluation over 20 subjects demonstrated that the estimated EFs correlate very well with those obtained from independent manual segmentations.