We evaluate the quality of CoMIR representations, as well as the success rate of registration for the combination of CoMIR and INSPIRE on three datasets: (i) A satellite dataset of Near-Infrared (NIR) and RGB images, (ii) A Magnetic Resonance Imaging (MRI) dataset of T1 and T2 2D images of brains, and (iii) a histological dataset of second-harmonics generation (SHG) and bright-field (BF) microscopy images of tissue samples. Furthermore, it introduces two new constraints to the contrastive loss called affine equivariance and deformable equivariance.

This project combines CoMIR and INSPIRE in order to perform multimodal deformable image registration. CoMIR is a type of image representation that is learned through contrastive learning and where the corresponding CoMIRs for a multimodal image pair share enough similarity in order to successfully apply rigid monomodal image registration on them.

A recently developed method named Contrastive Multimodal Image Representation for Registration (CoMIR) can be used totransform multimodal image pairs into the same representational space. However, relying on the absolute similarity of the image intensities, it is not suited for multimodal scenarios where this assumption does not hold. Intensity and Spatial Information-Based Deformable Image Registration (INSPIRE) is a robust framework for monomodal deformable image registration. In deformable imag e registration, the images are assumed to be misaligned with local deformations and therefore the registration must also be performed locally. Multimodal image registration is the specific case where the images are captured with different imaging systems. Image registration is the process of finding correspondences between images of the same or of similar scenes. 2022 (English) Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits Student thesis Abstract

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