Visualizing large-scale histopathological imaging and results of AI-driven digital pathology models and enabling interaction with the visualizations requires a high-performance server for handling image and annotations data. The RationAI group has developed an modular OpenSeaDragon-based visualization frontend system and needs to a backend server to support all the capabilities of the frontend. The goal of the work is to develop a server serving tiles to OpeaSeaDragon frontend with support for (a) efficient serving of multi-channel image images, from grayscale to RGB, (b) composing individual grayscale images into many-channel images for efficient network transfers, (c) support for serving and receiving vector annotations of the histopathological material. Student will also analyze possibilities to support OME-TIFF format for storing imaging data. The work will be done within the RationAI group in collaboration with Masaryk Memorial Cancer Institute. The goal of the work is to design a server solution to work with the OpenSeaDragon visualization system for efficient presentation of histopathological data and data coming from AI methods developed as a part of digital pathology solutions.