Cloud solution for Medical Image Analysis

My research has also pioneered a paradigm shift in the deployment of medical image analysis tools, moving from locally installed software toward fully accessible, cloud-based infrastructures. Traditionally, MRI processing pipelines require complex installation, configuration, and maintenance, as well as dedicated computational resources and technical expertise. These constraints significantly limit their adoption, particularly in clinical environments and non-specialized research settings.

To overcome these barriers, I developed volBrain, a cloud-based platform that provides fully automated MRI analysis through a web interface, following a Software-as-a-Service (SaaS) model. This approach removes the need for local installation, user training, or dedicated hardware, enabling seamless access to advanced image processing tools for a broad community of users.

My main contributions in this field are threefold:

• Development of a large-scale cloud infrastructure for neuroimaging: I co-designed and co-implemented a distributed platform capable of handling high-throughput MRI processing, ensuring scalability, robustness, and reproducibility. The system leverages shared computational resources to efficiently process large volumes of data while maintaining consistent performance.

• Integration of advanced image processing pipelines: I led the integration of a comprehensive suite of automated pipelines covering a wide range of neuroimaging tasks, including segmentation, volumetry, and quality-controlled reporting. These pipelines are continuously updated to incorporate state-of-the-art methodological advances.

• Global dissemination and large-scale usage: The volbrain platform (www.volbrain.net) has been widely adopted by the international community, with more than 800,000 MRI scans processed worldwide, demonstrating its robustness, usability, and clinical relevance. This large-scale usage has enabled the creation of unprecedented datasets and has facilitated reproducible research across institutions.

Overall, this work establishes a new framework for accessible, scalable, and reproducible medical image analysis, bridging methodological innovation with real-world deployment, and significantly accelerating the translation of advanced imaging tools into clinical and research practice.