  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.
