A deep learning python package for neuroimaging data. Focused on validated command-line tools you can use today. Created by the Quantitative Tumor Imaging Lab at the Martinos Center (Harvard-MIT Program in Health, Sciences, and Technology / Massachussets General Hospital).
DeepNeuro is an open-source toolset of deep learning applications for neuroimaging. We have several goals for this package:
- Provide easy-to-use command line tools for neuroimaging using deep learning.
- Create Docker containers for each tool and all out-of-package pre-processing steps, so they can each can be run without having install prerequisite libraries.
- Provide freely available deep learning models trained on a wealth of neuroimaging data.
- Provide training scripts and links to publically-available data to replicate the results of DeepNeuro's models.
- Provide implementations of popular models for medical imaging data, and pre-processed datasets for educational purposes.
This package is in an initial testing phase, and will be released soon. Currently, out glioblastoma segmentation package is available -- see details below for installation and usage instructions.
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Install the Docker Engine Utility for NVIDIA GPUs, AKA nvidia-docker. You can find installation instructions at their Github page, here: https://github.com/NVIDIA/nvidia-docker
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Pull the DeepNeuro Docker container from https://hub.docker.com/r/qtimlab/deepneuro_segment_gbm/. Use the command "docker pull qtimlab/deepneuro-segment_gbm"
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If you want to inspect the code, or run your Docker container with an DeepNeuro's python wrappers and command line tools, clone this repository ("git clone https://github.com/QTIM-Lab/DeepNeuro"), and install with the command "python setup.py install" in the directory you just cloned in to.
DeepNeuro is under active development, and you may run into errors or want additional features. Send any questions or requests for methods to abeers@mgh.harvard.edu. You can also submit a Github issue if you run into a bug.