A semi-automated myocardial segmentation method using CINE CMR images and non-rigid registration achieved an average Dice similarity coefficient of 84.36% in patients with myocardial fibrosis.
Does a semi-automated segmentation framework using CINE images provide accurate myocardial segmentation of T1-mapping CMR images in patients with myocardial infarction?
A novel semi-automated segmentation framework using CINE images provides accurate myocardial segmentation of T1-mapping CMR images in patients with ischemic myocardial fibrosis.
T1-mapping cardiac magnetic resonance (CMR) is a rapidly expanding non-invasive tool for quantitative assessment of myocardial fibrosis. To achieve both efficiency and reproducibility in quantification of T1 measures, automated myocardial boundary tracing is desirable. Accordingly, the application of robust segmentation algorithms for this modality are of significant interest. However, conventional algorithms may fail in myocardial segmentation of T1-mapping images due to low signal gradients at the endocardial-blood pool boundary. In this work, we propose using prior information from cinematic (CINE) CMR images toward accurate myocardial segmentation of native T1-mapping images, acquired using the shortened modified Look-Locker imaging (shMOLLI) technique. We use a three-step framework, which begins with pre-processing and resizing of both CINE and shMOLLI images. Next, we implement semi-automated segmentation of the myocardium on resized CINE images using a deformable model-based technique, via the freely available software Segment v2.2. The final step of our framework is registration and propagation of the CINE contours to corresponding (slice-matched) native shMOLLI images using a non-rigid registration technique based on a modality independent neighborhood descriptor (MIND). We validate our technique on 20 image sets obtained from 20 patients with confirmed myocardial fibrosis related to ischemic injury (myocardial infarction). Our method achieved an average Dice similarity coefficient (DSC) of 84.36% ± 4.03%, precision of 91.68% ± 7.89%, recall of 91.33% ± 8.41% and relative area error of 16.29% ± 8.58%.
Farrag et al. (Fri,) conducted a other in Myocardial fibrosis related to ischemic injury (myocardial infarction) (n=20). Semi-automated myocardial segmentation using CINE CMR images and non-rigid registration was evaluated on Dice similarity coefficient (DSC). A semi-automated myocardial segmentation method using CINE CMR images and non-rigid registration achieved an average Dice similarity coefficient of 84.36% in patients with myocardial fibrosis.