EVCMR: A tool for the quantitative evaluation and visualization of cardiac MRI data
- Authors
- Kim, Y.-C.[Kim, Y.-C.]; Kim, K.R.[Kim, K.R.]; Choi, K.[Choi, K.]; Kim, M.[Kim, M.]; Chung, Y.[Chung, Y.]; Choe, Y.H.[Choe, Y.H.]
- Issue Date
- Aug-2019
- Publisher
- Elsevier Ltd
- Keywords
- Deep learning; Heart; Image segmentation; MRI; Python; Visualization
- Citation
- Computers in Biology and Medicine, v.111
- Indexed
- SCIE
SCOPUS
- Journal Title
- Computers in Biology and Medicine
- Volume
- 111
- URI
- https://scholarworks.bwise.kr/skku/handle/2021.sw.skku/14990
- DOI
- 10.1016/j.compbiomed.2019.103334
- ISSN
- 0010-4825
- Abstract
- Quantitative evaluation of diseased myocardium in cardiac magnetic resonance imaging (MRI) plays an important role in the diagnosis and prognosis of cardiovascular disease. The development of a user interface with state-of-the-art techniques would be beneficial for the efficient post-processing and analysis of cardiac images. The aim of this study was to develop a custom user interface tool for the quantitative evaluation of the short-axis left ventricle (LV) and myocardium. Modules for cine, perfusion, late gadolinium enhancement (LGE), and T1 mapping data analyses were developed in Python, and a module for three-dimensional (3D) visualization was implemented using PyQtGraph library. The U-net segmentation and manual contour correction in the user interface were effective in generating reference myocardial segmentation masks, which helped obtain labeled data for deep learning model training. The proposed U-net segmentation resulted in a mean Dice score of 0.87 (±0.02) in cine diastolic myocardial segmentation. The LV mass measurement of the proposed method showed good agreement with that of manual segmentation (intraclass correlation coefficient = 0.97, mean difference and 95% Bland-Altman limits of agreement = 4.4 ± 12.2 g). C++ implementation of voxel-wise T1 mapping and its binding via pybind11 led to a significant computational gain in calculating the T1 maps. The 3D visualization enabled fast user interactions in rotating and zooming-in/out of the 3D myocardium and scar transmurality. The custom tool has the potential to provide a fast and comprehensive analysis of the LV and myocardium from multi-parametric MRI data in clinical settings. © 2019 Elsevier Ltd
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