Abstract
The use of MR images in medical image analysis from different centers in clinical applications and medical research has grown in popularity. However, challenges arise due to inherent variability between centers, leading to domain shift issues that reduce the reliability and robustness of the analysis results. Furthermore, the lack of suitable tools for analyzing domain shift hampers the progress of developing and validating domain adaptation and harmonization techniques. Utilizing pre-trained deep models as feature extractors, we introduce a novel framework called Deep Domain Shift analyzer for MRI (DeepDSMRI), designed explicitly to comprehend the extent of domain shift in MRI datasets. DeepDSMRI provides adequate insights into the existence of domain shift for diverse MRI modalities, including structural, functional, and diffusion-weighted images. The proposed framework incorporates visualization tools (e.g., t-SNE and UMAP) to illustrate grouping similar data and isolating dissimilar data into distinct clusters. Moreover, the quantitative analysis measures the classification accuracy between domains and the domain shift distance. The efficacy of the proposed DeepDSMRI is demonstrated through experimental assessments conducted on seven extensive multi-center neuroimaging databases. The source code is available at (https://github.com/rkushol/DeepDSMRI).
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Acknowledgments
This study has been supported by the Prime Minister Fellowship Bangladesh, Canadian Institutes of Health Research (CIHR), ALS Society of Canada, Brain Canada Foundation, and Natural Sciences and Engineering Research Council of Canada (NSERC).
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Appendix
Appendix
Visualization of feature representation for a 2D MRI slice using various layers of the pre-trained ResNet50 deep model. The images from left to right display the output of feature representation for layers one to five, respectively.
Visualizing domain shift via UMAP plots for the CALSNIC1, CALSNIC2, and ADNI2 datasets using the proposed DeepDSMRI framework. Notably, in comparison to the t-SNE method, the UMAP approach tends to produce a more clustered representation.
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Kushol, R., Kalra, S., Yang, YH. (2024). DeepDSMRI: Deep Domain Shift Analyzer for MRI. In: Yap, M.H., Kendrick, C., Behera, A., Cootes, T., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2024. Lecture Notes in Computer Science, vol 14859. Springer, Cham. https://doi.org/10.1007/978-3-031-66955-2_6
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