The deployment of deep learning algorithms in clinical practice faces challenges in data privacy and local hardware constraints. This work presents the tools and design choices of a browser-based edge computing framework to address these challenges. We leverage this framework for 3D medical image segmentation from computed tomography and characterize its speed, memory, and limitations across various operating systems and browsers. Our platform deploys deep learning-based segmentation of a 256×256×256 volume with an average runtime of 80 seconds and average memory usage of 1.5 GB on Firefox, Chrome, and Microsoft Edge using consumer-level laptops
The paper has been published at SPIE Medical Imaging.
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