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 (this http URL) 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.

Brainchop 是一款前瞻性的浏览器神经影像工具，采用预训练的全脑深度学习模型，可实现对结构性 MRI 的体积分析，无需技术专长或繁琐的设置程序，同时保护端用户的数据隐私和居留权；此外，该前端工具具有可扩展性、低延迟、易于操作、跨平台兼容性和更高的可访问性。本论文概述了 Brainchop 的处理流程，并评估了模型在各种软件和硬件配置下的性能，结果表明在资源受限的浏览器环境下，借助稳健的 MeshNet 架构，客户端处理体积数据是可行的。