MassVis ion: An open-source end-to-end platform for AI-driven mass spectrometry image analysis

Abstract Mass spectrometry imaging (MSI) combines spatial and spectral data to reveal detailed molecular compositions within biological samples. Despite their immense potential, MSI workflows are hindered by the complexity and high dimensionality of the data, making their analysis computationally intensive and often requiring expertise in coding. Existing tools frequently lack the integration needed for seamless, scalable, and end-to-end workflows, forcing researchers to rely on local solutions or multiple platforms, hindering efficiency and accessibility. We introduce MassVision, a comprehensive soft-ware platform for MSI analysis. Built on the 3D Slicer ecosystem, MassVision integrates MSI-specific functionalities while addressing general user requirements for accessibility and usability. Its intuitive interface lowers barriers for researchers with varying levels of computational expertise, while its scalability supports high-throughput studies and multi-slide datasets. Key functionalities include visualization, co-localization, dataset curation, dataset merging, spectral and spatial preprocessing, AI model training, and AI deployment on full MSI data. We detail the workflow and functionalities of MassVision and demonstrate its effectiveness through different experimental use cases such as exploratory data analysis, ion identification, and tissue-type classification, on in-house and publicly available data from different MSI modalities. These use cases underscore the MassVision’s ability to seamlessly integrate MSI data handling steps into a single platform, and highlight its potential to reveal new insights and structures when examining biological samples. By combining cutting-edge functionality with user-centric design, MassVision addresses longstanding challenges in MSI data analysis and provides a robust tool for advancing the user’s ability to achieve biologically-meaningful insights from MSI data. Competing Interest Statement The authors have declared no competing interest.