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Blood vessel segmentation in the browser
VesselBoost is a browser-based tool for automatic segmentation of blood vessels from MRI angiography using a 3D UNet model.
All computation runs locally in your browser using ONNX Runtime Web. No data is uploaded to any server.
VesselBoost runs entirely in your web browser using WebAssembly and ONNX Runtime. All processing occurs locally on your machine.
VesselBoost uses ONNX Runtime Web to run deep learning models directly in the browser via WebAssembly. Your NIfTI or DICOM files are read into browser memory, processed locally, and results are displayed without any network transfer.
VesselBoost does not use analytics, tracking, cookies, or any external services. The only network requests are loading the application itself and downloading model files (which are cached locally).
Marshall K, et al. VesselBoost: A Python Toolbox for Small Blood Vessel Segmentation.
https://github.com/KMarshallX/VesselBoost/VesselBoost uses the following methods and tools:
Paszke A, et al. PyTorch: An Imperative Style, High-Performance Deep Learning Library. NeurIPS 2019.
https://pytorch.orgMicrosoft. ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator.
https://onnxruntime.aiLi X, Morgan PS, Ashburner J, Smith J, Rorden C. The first step for neuroimaging data analysis: DICOM to NIfTI conversion. J Neurosci Methods. 2016;264:47-56.
https://github.com/rordenlab/dcm2niixNiiVue Contributors. NiiVue: a WebGL2 medical image viewer.
https://github.com/niivue/niivue