https://github.com/ad12/dosma
An AI-powered open-source medical image analysis toolbox
https://github.com/ad12/dosma
deep-learning medical-image-analysis mri musculoskeletal
Last synced: 8 months ago
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An AI-powered open-source medical image analysis toolbox
- Host: GitHub
- URL: https://github.com/ad12/dosma
- Owner: ad12
- License: gpl-3.0
- Created: 2018-09-06T01:45:40.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2024-08-19T23:18:04.000Z (about 1 year ago)
- Last Synced: 2025-02-16T02:51:43.522Z (8 months ago)
- Topics: deep-learning, medical-image-analysis, mri, musculoskeletal
- Language: Python
- Homepage:
- Size: 10.9 MB
- Stars: 61
- Watchers: 5
- Forks: 14
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
README
# DOSMA: Deep Open-Source Medical Image Analysis
[](https://www.gnu.org/licenses/gpl-3.0)
[](https://codecov.io/gh/ad12/DOSMA)
[](https://dosma.readthedocs.io/en/latest/?badge=latest)
[Documentation](http://dosma.readthedocs.io/) | [Questionnaire](https://forms.gle/sprthTC2swyt8dDb6) | [DOSMA Basics Tutorial](https://colab.research.google.com/drive/1zY5-3ZyTBrn7hoGE5lH0IoQqBzumzP1i?usp=sharing)
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](https://elastix.lumc.nl/download.php).
Details can be found in the [setup documentation](https://dosma.readthedocs.io/en/latest/general/installation.html#setup).
To install DOSMA, run:
```bash
pip install dosma
# To install with AI support
pip install dosma[ai]
```
If you would like to contribute to DOSMA, we recommend you clone the repository and
install DOSMA with `pip` in editable mode.
```bash
git clone git@github.com:ad12/DOSMA.git
cd DOSMA
pip install -e '.[dev,docs]'
make dev
```
To run tests, build documentation and contribute, run
```bash
make autoformat test build-docs
```
## Features
### Simplified, Efficient I/O
DOSMA provides efficient readers for DICOM and NIfTI formats built on nibabel and pydicom. Multi-slice DICOM data can be loaded in
parallel with multiple workers and structured into the appropriate 3D volume(s). For example, multi-echo and dynamic contrast-enhanced (DCE) MRI scans have multiple volumes acquired at different echo times and trigger times, respectively. These can be loaded into multiple volumes with ease:
```python
import dosma as dm
multi_echo_scan = dm.load("/path/to/multi-echo/scan", group_by="EchoNumbers", num_workers=8, verbose=True)
dce_scan = dm.load("/path/to/dce/scan", group_by="TriggerTime")
```
### Data-Embedded Medical Images
DOSMA's [MedicalVolume](https://dosma.readthedocs.io/en/latest/generated/dosma.MedicalVolume.html#dosma.MedicalVolume) data structure supports array-like operations (arithmetic, slicing, etc.) on medical images while preserving spatial attributes and accompanying metadata. This structure supports NumPy interoperability, intelligent reformatting, fast low-level computations, and native GPU support. For example, given MedicalVolumes `mvA` and `mvB` we can do the following:
```python
# Reformat image into Superior->Inferior, Anterior->Posterior, Left->Right directions.
mvA = mvA.reformat(("SI", "AP", "LR"))
# Get and set metadata
study_description = mvA.get_metadata("StudyDescription")
mvA.set_metadata("StudyDescription", "A sample study")
# Perform NumPy operations like you would on image data.
rss = np.sqrt(mvA**2 + mvB**2)
# Move to GPU 0 for CuPy operations
mv_gpu = mvA.to(dosma.Device(0))
# Take slices. Metadata will be sliced appropriately.
mv_subvolume = mvA[10:20, 10:20, 4:6]
```
### Built-in AI Models
DOSMA is built to be a hub for machine/deep learning models. A complete list of models and corresponding publications can be found [here](https://dosma.readthedocs.io/en/latest/models.html).
We can use one of the knee segmentation models to segment a MedicalVolume `mv` and model
`weights` [downloaded locally](https://dosma.readthedocs.io/en/latest/installation.html#segmentation):
```python
from dosma.models import IWOAIOAIUnet2DNormalized
# Reformat such that sagittal plane is last dimension.
mv = mv.reformat(("SI", "AP", "LR"))
# Do segmentation
model = IWOAIOAIUnet2DNormalized(input_shape=mv.shape[:2] + (1,), weights_path=weights)
masks = model.generate_mask(mv)
```
### Parallelizable Operations
DOSMA supports parallelization for compute-heavy operations, like curve fitting and image registration.
Image registration is supported thru the [elastix/transformix](https://elastix.lumc.nl/download.php) libraries. For example we can use multiple workers to register volumes to a target, and use the registered outputs for per-voxel monoexponential fitting:
```python
# Register images mvA, mvB, mvC to target image mv_tgt in parallel
_, (mvA_reg, mvB_reg, mvC_reg) = dosma.register(
mv_tgt,
moving=[mvA, mvB, mvC],
parameters="/path/to/elastix/registration/file",
num_workers=3,
return_volumes=True,
show_pbar=True,
)
# Perform monoexponential fitting.
def monoexponential(x, a, b):
return a * np.exp(b*x)
fitter = dosma.CurveFitter(
monoexponential,
num_workers=4,
p0={"a": 1.0, "b": -1/30},
)
popt, r2 = fitter.fit(x=[1, 2, 3, 4], [mv_tgt, mvA_reg, mvB_reg, mvC_reg])
a_fit, b_fit = popt[..., 0], popt[..., 1]
```
## Citation
```
@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 27th Annual Meeting ISMRM, Montreal},
pages={1135},
year={2019}
}
```
In addition to DOSMA, please also consider citing the work that introduced the method used for analysis.