Multiplex spatial proteomics technologies generate large, high-dimensional microscopy datasets that require computationally intensive image processing and downstream spatial analysis. This project aims to develop and evaluate scalable computational workflows for the analysis of spatial proteomics imaging data generated within collaborative biomedical research projects. The work will focus on image preprocessing, cell segmentation, phenotyping and annotation, quality control, feature extraction, and downstream spatial analysis, including cell composition, marker co-expression, spatial distances, neighborhood analysis, and tissue-region-based quantification. GPU-accelerated methods will also be evaluated for deep-learning-based image segmentation and feature extraction. A major objective is to establish reproducible workflows that can scale from individual test samples to larger cohorts containing tens of high-resolution tissue samples. The computational resources will initially be used to benchmark and optimize existing workflows on representative datasets, followed by batch processing of larger spatial proteomics cohorts and evaluation of GPU-accelerated image-analysis approaches. The project will contribute to more efficient and reproducible analysis of multiplex tissue imaging data and facilitate the development of computational analysis capabilities for spatial biology research.