3D brain registration with intensity shift robustness

Authors/Creators

Hassan Mahmood, Edith Cowan UniversityFollow
Asim Iqbal
Syed M. S. Islam, Edith Cowan UniversityFollow
Syed A. A. Shah, Edith Cowan UniversityFollow

Abstract

Technological advances in medical imaging are enabling us to understand healthcare datasets in great detail. Machine Learning enabled methods, specifically, deep neural networks are continuously achieving benchmark performances in terms of accuracy and computational efficiency. However, the lack of agreed-upon standard procedures, variations in the devices by different vendors, and artifacts induced by the physical phenomenon in the sensors make the data inconsistent and noisy. These variations in the data are detrimental to the performance of learning-based methods. In this study, we analyze the behavior of traditional and deep learning-based image registration methods and explore strategies to handle the problem of intensity distributional shifts without compromising the performance. To achieve this, we propose an intensity-based loss function and demonstrate that the models trained with our proposed loss function are better at handling unseen data from different sites using machines from different vendors. In addition, our trained model is superior in preserving the boundaries of anatomical regions after registration.

RAS ID

58305

Document Type

Conference Proceeding

Date of Publication

2023

School

School of Science

Copyright

subscription content

Publisher

IEEE

Comments

Mahmood, H., Iqbal, A., Islam, S. M. S., & Shah, S. A. A. (2023). 3D brain registration with intensity shift robustness. In 2023 IEEE International Conference on Image Processing (ICIP) (pp. 2805-2809). IEEE. https://doi.org/10.1109/ICIP49359.2023.10222341

Recommended Citation

Mahmood, H., Iqbal, A., Islam, S. M., & Shah, S. A. (2023). 3D brain registration with intensity shift robustness. DOI: https://doi.org/10.1109/ICIP49359.2023.10222341