Metadata-Version: 2.1
Name: pyAutoRef
Version: 2.2.0
Summary: Python pakage to perfom AutoRef (prostate T2w MRI dual reference tissue [fat and muscle] normalization).
Home-page: https://github.com/MohammedSunoqrot/pyAutoRef
Author: Mohammed R. S. Sunoqrot
Author-email: mohammed.sunoqrot@ntnu.no
Project-URL: Bug Tracker, https://github.com/MohammedSunoqrot/pyAutoRef/issues
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy
Requires-Dist: SimpleITK
Requires-Dist: Pillow
Requires-Dist: pydicom
Requires-Dist: scikit-image
Requires-Dist: onnxruntime
Requires-Dist: matplotlib>=3.0.0

![PyPI](https://img.shields.io/pypi/v/pyAutoRef)

# pyAutoRef

This is the python version of the
"Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition"

This is an automated method for dual-reference tissue (fat and muscle) normalization of T2-weighted MRI for the prostate.

The method was developed at the CIMORe group at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway.
[https://www.ntnu.edu/isb/cimore]

For detailed information about this method, please read our paper: [https://link.springer.com/article/10.1007%2Fs10334-020-00871-3]

# Note

The provided algorithm was developed for research use and was NOT meant to be used in clinic.

# Structure

```
pyAutoRef/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── pyAutoRef/
│       ├── __init__.py
│       ├── autoref.py
│       ├── pre_processing.py
│       ├── object_detection.py
│       ├── post_processing.py
│       ├── normalization.py
│       ├── utils.py
│       ├── MANIFEST.in
│       └── model.onnx
└── tests/
```

# Installation

You can install the package either from pip or using pip or the files in GitHub repository [https://github.com/MohammedSunoqrot/pyAutoRef]

## pip

Simply type:

```
pip install pyAutoRef
```

## GitHub

- Clone the GitHub repository

  _From command line_

  ```
  git clone https://github.com/MohammedSunoqrot/pyAutoRef.git
  ```

- Change directory to the clones folder (unzip if needed) and type
  ```
  pip install .
  ```

# Difference Note

This python version is differ than the originally published MATLAB version [https://github.com/ntnu-mr-cancer/AutoRef] in terms:

- It accepts all kind of SimpleITK supported image format.
- It uses YOLOv8 model for object detector instead of ACF.

## Diviation from the original published, MATLAB-based method

**`VERSION 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4`**

- YOLOv8 model for object detector trained using the same data and bounding boxes used to train ACF in the original/MATLAB code. _No Data deviation, but needed to meantioned_.
- The detection was performed on all slices for both fat and muscle.

**`VERSION 2.0.0 and above`**

- YOLOv8 model for object detector trained using images from 823 cases (4 centers, 3 centers data is private and 1 is public which is the PROSTATE158 training dataset) in addition to the same data used to train the original detector.
- For detection for both fat and muscle the first 15% and the last 15% of slices were not looked at for detection.
- In detection post-processing, the kernel of opening radius of the detected object was set to 5 pixels.
- In detection post-processing, a morphological erosion step follows the opening has been added with kernel radius of 5 pixels.
- The 95th and 5th percentile of the fat and muscle intensities, respectively, were used to normalize the image.

**`VERSION 2.1.0 and above`**

- In case of non-detected fat or muscle objects, the 3 middel slices will be selected, and the normalization will be done using the intensity be calculated using the 95th and 5th percentile of the entire pre-prccessed 3 slices.

# How to cite AutoRef/pyAutoRef

In case of using or refering to AutoRef/pyAutoRef, please cite it as:

```
Sunoqrot, M.R.S., Nketiah, G.A., Selnæs, K.M. et al. Automated reference tissue normalization of T2-weighted MR images of the prostate using object recognition. Magn Reson Mater Phy 34, 309–321 (2021). [https://doi.org/10.1007/s10334-020-00871-3]
```

# How to use pyAutoRef

To perform AutoRef normalization, you first need to import the `autoref` function.
You can do it by calling `from pyAutoRef import autoref`

## `autoref` Function

- Parameters:

  - input_image (SimpleITK.Image, str): The input image as SimpleITK.Image OR The file path to the input 3D image (any supported SimpleITK format) or to the DICOM folder.
  - output_image_path (str, optional): The file path to save the normalized output image to any supported SimpleITK format. If None, the image will not be saved.

- Returns:
  - normalized_image (SimpleITK.Image): The normalized 3D image.

## Supported input/output formats

- Path to DICOM Series.
- Path to images of all the medical [images formats supported by SimpleITK](https://simpleitk.readthedocs.io/en/v2.2.0/IO.html).
- [SimpleITK.Image] (https://simpleitk.org/SimpleITK-Notebooks/01_Image_Basics.html).

**_DICOM Series is recognized when there is no file extension_**

### Examples of usage:

**_Example (input: SiTK Image variable within the code, output: medical image format -Path to write- ):_**

```
import SimpleITK as sitk
from pyAutoRef import autoref

file_path = r"C:\Data\Case10_t2.nii.gz"

input_image = sitk.ReadImage(file_path)

output_image_path = r"C:\Data\Case10_t2_normalized.nii.gz"

autoref(input_image, output_image_path)
```

**_Example (input: SiTK Image variable within the code, output: SiTK Image variable within the code):_**

```
import SimpleITK as sitk
from pyAutoRef import autoref

input_image = sitk.ReadImage(file_path)

normalized_image = autoref(input_image)
```

**_Example (input: medical image format, output: SiTK Image variable within the code):_**

```
from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2.nii.gz"

normalized_image = autoref(input_image_path)
```

**_Example (input: medical image format, output: medical image format):_**

```
from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2.nii.gz"
output_image_path = r"C:\Data\Case10_t2_normalized.nii.gz"

autoref(input_image_path, output_image_path)
```

**_Example (input: medical image format, output: DICOM Series):_**

```
from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2.nii.gz"
output_image_path = r"C:\Data\Case10_t2_normalized"

autoref(input_image_path, output_image_path)
```

**_Example (input: DICOM Series, output: medical image format):_**

```
from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2"
output_image_path = r"C:\Data\Case10_t2_normalized.nii.gz"

autoref(input_image_path, output_image_path)

```

**_Example (input: DICOM Series, output: DICOM Series):_**

```
from pyAutoRef import autoref

input_image_path = r"C:\Data\Case10_t2"
output_image_path = r"C:\Data\Case10_t2_normalized"

autoref(input_image_path, output_image_path)
```
