  Performing volumetric image processing directly within the browser,
particularly with medical data, presents unprecedented challenges compared to
conventional backend tools. These challenges arise from limitations inherent in
browser environments, such as constrained computational resources and the
availability of frontend machine learning libraries. Consequently, there is a
shortage of neuroimaging frontend tools capable of providing comprehensive
end-to-end solutions for whole brain preprocessing and segmentation while
preserving end-user data privacy and residency. In light of this context, we
introduce Brainchop (http://www.brainchop.org) as a groundbreaking in-browser
neuroimaging tool that enables volumetric analysis of structural MRI using
pre-trained full-brain deep learning models, all without requiring technical
expertise or intricate setup procedures. Beyond its commitment to data privacy,
this frontend tool offers multiple features, including scalability, low
latency, user-friendly operation, cross-platform compatibility, and enhanced
accessibility. This paper outlines the processing pipeline of Brainchop and
evaluates the performance of models across various software and hardware
configurations. The results demonstrate the practicality of client-side
processing for volumetric data, owing to the robust MeshNet architecture, even
within the resource-constrained environment of web browsers.
