An AI-powered open-source medical image analysis toolbox
Project description
DOSMA: Deep Open-Source Medical Image Analysis
Documentation | Questionnaire | DOSMA Basics Tutorial
DOSMA is an AI-powered Python library for medical image analysis. This includes, but is not limited to:
- image processing (denoising, super-resolution, registration, segmentation, etc.)
- quantitative fitting and image analysis
- anatomical visualization and analysis (patellar tilt, femoral cartilage thickness, etc.)
We hope that this open-source pipeline will be useful for quick anatomy/pathology analysis and will serve as a hub for adding support for analyzing different anatomies and scan sequences.
Installation
DOSMA requires Python 3.6+. The core module depends on numpy, nibabel, nipype, pandas, pydicom, scikit-image, scipy, PyYAML, and tqdm.
Additional AI features can be unlocked by installing tensorflow and keras. To enable built-in registration functionality, download elastix. Details can be found in the setup documentation.
To install DOSMA, run:
pip install dosma
If you would like to contribute to DOSMA, we recommend you clone the repository and
install DOSMA with pip
in editable mode.
git clone git@github.com:ad12/DOSMA.git
cd DOSMA
pip install -e '.[dev]'
make dev
To run tests, build documentation and contribute, run
make autoformat test build-docs
How to Cite
@inproceedings{desai2019dosma,
Title={DOSMA: A deep-learning, open-source framework for musculoskeletal MRI analysis.},
Author = {Desai, Arjun D and Barbieri, Marco and Mazzoli, Valentina and Rubin, Elka and Black, Marianne S and Watkins, Lauren E and Gold, Garry E and Hargreaves, Brian A and Chaudhari, Akshay S},
Booktitle={Proc. Intl. Soc. Mag. Reson. Med},
Volume={27},
Number={1106},
Year={2019}
}
In addition to DOSMA, please also consider citing the work that introduced the method used for analysis.
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