We propose an efficient algorithm for computing MI for all discrete displacements (formalized as the cross-mutual information function (CMIF)), which is based on cross-correlation computed in the frequency domain.
We compare the performance of four Generative Adversarial Network (GAN)-based methods and one contrastive representation learning method, subsequently combined with two representative monomodal registration methods, to judge the effectiveness of modality translation for multimodal image registration.
We also evaluate the method on four benchmark datasets of 3D images of brains, for a total of 2088 pairwise registrations; a comparison with 15 other state-of-the-art methods reveals that INSPIRE provides the best overall performance.
We propose contrastive coding to learn shared, dense image representations, referred to as CoMIRs (Contrastive Multimodal Image Representations).
The pipeline consists of fully convolutional regression-based nucleus detection, followed by per-cell focus selection, and CNN based classification.
The method exhibits greater robustness and higher accuracy than similarity measures in common use, when inserted into a standard gradient-based registration framework available as part of the open source Insight Segmentation and Registration Toolkit (ITK).