Left ventricle segmentation in cardiac MRI using data-driven priors and temporal correlations
© Jia et al; licensee BioMed Central Ltd. 2010
Published: 21 January 2010
Cardiac MRI has been widely adopted in the study of heart transplant rejection using small animal models. Due to limited image quality, quantitative assessments of such studies are generally performed through manual segmentation. Therefore, it is desirable to develop a reliable and accurate segmentation algorithm.
The goal of this study is to develop an automated algorithm for the segmentation of the left ventricle (LV) of both native and transplanted rat hearts in cardiac MR images of rats.
Our level set based method combines data-driven priors, temporal correlation, as well as texture and intensity information for segmentation.
1. Prior extraction
2. Temporal correlation
Instead of segmenting each image independently, we incorporate the temporal correlation among frames. Given one reference frame with reliable segmentation, the myocardial contours in the reference frame are propagated to other frames in the same sequence by non-rigid registration. Based on registration error, we then generate a confidence map for each frame, indicating the reliability of the propagated contours.
We have tested the proposed method on 120 MR images of both native and transplanted rat hearts. All MRI scans were performed by a Brucker AVANCE DRX 4.7 T system with the following imaging parameters: FOV = 4 cm; image resolution = 256 × 256 pixels; TR = a cardiac cycle; TE = 5.5 msec.
To quantitatively evaluate the segmentation accuracy, we measure the area similarity between myocardial masks obtained by the proposed method and their manual counterparts. The area similarities have mean value of 0.88 with standard deviation 0.05, which indicates very good match between the ground truth and our segmentation results.
Our experimental results suggest that the proposed method is robust and accurate in segmenting LV of both native and transplanted rat hearts in cardiac MR images.
This article is published under license to BioMed Central Ltd.