Semi-automated myocardial segmentation in native T1-mapping CMR using deformable non-rigid registration of CINE images

Nadia A. Farrag; James A. White M.D.; Eranga Ukwatta

doi:10.1117/12.2513054

15 March 2019 Semi-automated myocardial segmentation in native T1-mapping CMR using deformable non-rigid registration of CINE images

Nadia A. Farrag, James A. White M.D., Eranga Ukwatta

Author Affiliations +

Proceedings Volume 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging; 109531C (2019) https://doi.org/10.1117/12.2513054
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

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%.

Conference Presentation

Citation Download Citation

Nadia A. Farrag, James A. White M.D., and Eranga Ukwatta "Semi-automated myocardial segmentation in native T1-mapping CMR using deformable non-rigid registration of CINE images", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531C (15 March 2019); https://doi.org/10.1117/12.2513054

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