Global assessment of cardiac function using image statistics in MRI.
Med Image Comput Comput Assist Interv
; 15(Pt 2): 535-43, 2012.
Article
en En
| MEDLINE
| ID: mdl-23286090
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.
Buscar en Google
Bases de datos:
MEDLINE
Asunto principal:
Volumen Sistólico
/
Algoritmos
/
Imagen por Resonancia Magnética
/
Interpretación de Imagen Asistida por Computador
/
Disfunción Ventricular Izquierda
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Año:
2012
Tipo del documento:
Article
País de afiliación:
Canadá