Photonic Data Science

Background: Multimodal histology image registration is a process that transforms into a common coordinate system two or more images obtained from diferent microscopy modalities. The combination of information from various modalities can contribute to a comprehensive understanding of tissue specimens, aiding in more accurate diagnoses, and improved research insights. Multimodal image registration in histology samples presents a signifcant challenge due to the inherent diferences in characteristics and the need for tailored optimization algorithms for each modality. Results: We developed MMIR a cloud-based system for multimodal histological image registration, which consists of three main modules: a project manager, an algorithm manager, and an image visualization system. Conclusion: Our software solution aims to simplify image registration tasks with a user-friendly approach. It facilitates efective algorithm management, responsive web interfaces, supports multi-resolution images, and facilitates batch image registration. Moreover, its adaptable architecture allows for the integration of custom algorithms, ensuring that it aligns with the specifc requirements of each modality combination. Beyond image registration, our software enables the conversion of segmented annotations from one modality to another.