# DOSMA: Deep Open-Source Medical Image Analysis

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[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.