Exploring intensity invariance in deep neural networks for brain image registration
Document Type
Conference Proceeding
Publication Title
2020 Digital Image Computing: Techniques and Applications, DICTA
Publisher
IEEE
School
School of Science
RAS ID
35489
Abstract
Image registration is a widely-used technique in analysing large scale datasets that are captured through various imaging modalities and techniques in biomedical imaging such as MRI, X-Rays, etc. These datasets are typically collected from various sites and under different imaging protocols using a variety of scanners. Such heterogeneity in the data collection process causes inhomogeneity or variation in intensity (brightness) and noise distribution. These variations play a detrimental role in the performance of image registration, segmentation and detection algorithms. Classical image registration methods are computationally expensive but are able to handle these artifacts relatively better. However, deep learning-based techniques are shown to be computationally efficient for automated brain registration but are sensitive to the intensity variations. In this study, we investigate the effect of variation in intensity distribution among input image pairs for deep learning-based image registration methods. We find a performance degradation of these models when brain image pairs with different intensity distribution are presented even with similar structures. To overcome this limitation, we incorporate a structural similarity-based loss function in a deep neural network and test its performance on the validation split separated before training as well as on a completely unseen new dataset. We report that the deep learning models trained with structure similarity-based loss seems to perform better for both datasets. This investigation highlights a possible performance limiting factor in deep learning-based registration models and suggests a potential solution to incorporate the intensity distribution variation in the input image pairs. Our code and models are available at https://github.com/hassaanmahmood/DeepIntense.
DOI
10.1109/DICTA51227.2020.9363409
Access Rights
subscription content
Comments
Mahmood, H., Iqbal, A., & Islam, S. M. S. (2020 November - December). Exploring intensity invariance in deep neural networks for brain image registration [Paper presentation]. 2020 Digital Image Computing: Techniques and Applications, (DICTA), Melbourne, Australia. https://doi.org/10.1109/DICTA51227.2020.9363409