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This study explores the application of deep learning to the segmentation of DENSE cardiovascular magnetic resonance (CMR) images, which is an important step in the analysis of cardiac deformation and may help in the diagnosis of heart conditions. A self-adapting method based on the nnU-Net framework is introduced to enhance the accuracy of DENSE-MR image segmentation, with a particular focus on the left ventricle myocardium (LVM) and left ventricle cavity (LVC), by leveraging the phase information in the cine DENSE-MR images. Two models are built and compared: 1) ModelM, which uses only the magnitude of the DENSE-MR images; and 2) ModelMP, which incorporates magnitude and phase images. DENSE-MR images from 10 human volunteers processed using the DENSE-Analysis MATLAB toolbox were included in this study. The two models were trained using a 2D UNet-based architecture with a loss function combining the Dice similarity coefficient (DSC) and cross-entropy. The findings show the effectiveness of leveraging the phase information with ModelMP resulting in a higher DSC and improved image segmentation, especially in challenging cases, e.g., at early systole and with basal and apical slices.
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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020
Image synthesis from corrupted contrasts increases the diver- sity of diagnostic information available for many neurological diseases. Recently the image-to-image translation has experienced signi cant lev- els of interest within medical research, beginning with the successful use of the Generative Adversarial Network (GAN) to the introduction of cyclic constraint extended to multiple domains. However, in current ap- proaches, there is no guarantee that the mapping between the two image domains would be unique or one-to-one. In this paper, we introduce a novel approach to unpaired image-to-image translation based on the invertible architecture. The invertible property of the ow-based architecture assures a cycle-consistency of image-to-image translation without additional loss functions. We utilize the temporal informa- tion between consecutive slices to provide more constraints to the optimization for transforming one domain to another in un- paired volumetric medical images. To capture temporal structures in the medical images, we explore the displacement between the consec- utive slices using a deformation eld. In our approach, the deformation eld is used as a guidance to keep the translated slides realistic and con- sistent across the translation. The experimental results have shown that the synthesized images using our proposed approach are able to archive a competitive performance in terms of mean squared error, peak signal- to-noise ratio, and structural similarity index when compared with the existing deep learning-based methods on three standard datasets, i.e. HCP, MRBrainS13 and Brats2019.
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- Award ID(s):
- 1946391
- PAR ID:
- 10320012
- Date Published:
- Journal Name:
- International Conference on Medical Image Computing and Computer-Assisted Intervention
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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