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https://github.com/cerr/pyCERR

Computational Environment for Radiological Research
https://github.com/cerr/pyCERR

Last synced: 19 days ago
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Computational Environment for Radiological Research

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# pyCERR - A Python-based Computational Environment for Radiological Research



pyCERR provides convenient data structure for imaging metadata and their associations. Utilities are provided to to extract, transform, organize metadata and visualize results of image processing for image and dosimetry features, image processing for AI model training and inference.



## Install Miniconda and Git

It is recommended to install CERR in an isolated environment such as Anaconda or VENV from GitHub repository. Please refer to 

1. https://docs.conda.io/projects/miniconda/en/latest/miniconda-install.html for installing Miniconda and 

2. https://git-scm.com/downloads for installing Git on your system.



## Install pyCERR



Launch Miniconda terminal, create a Conda environment with Python 3.8 and install CERR. Note that CERR requires Python version >= 3.8 to use Napari Viewer.

````

conda create -y --name pycerr python=3.11

conda activate pycerr

pip install "pyCERR[napari] @ git+https://github.com/cerr/pyCERR"

````    

The above steps will install CERR under testcerr/Lib/site-packages. 



Install Jupyter Lab or Notebook to try out example notebooks.

````

pip install jupyterlab

````



## Example Notebooks

Example notebooks are hosted at https://github.com/cerr/pyCERR-Notebooks/ . Clone this repository to use notebooks as a starting point.

````

git clone https://github.com/cerr/pyCERR-Notebooks.git

````



## Example snippets



Run python from the above Anaconda environment and try out the following code samples.



### import modules for planC and viewer

    import numpy as np

    from cerr import plan_container as pc

    from cerr import viewer as vwr



### Read DICOM directory contents to planC

    dcm_dir = r"\\path\to\Data\dicom\directory"

    planC = pc.load_dcm_dir(dcm_dir)

    

### Read NifTi scan to planC

    scanNiiFileName = r"\\path\to\Data\scan.nii.gz"

    planC = pc.load_nii_scan(scanNiiFileName, imageType = "CT SCAN")



### Read NifTi scan to planC by specifying the orientation

    planC = pc.load_nii_scan(scanNiiFileName, imageType = "CT SCAN", direction='HFS')



### Read NifTi scan to planC by appending to an existing planC

    planC = pc.load_nii_scan(scanNiiFileName, imageType = "CT SCAN", direction='HFS', planC)

    

### Read NifTi segmentation to planC

    structNiiFileName = r"\\path\to\Data\structure.nii.gz"

    assocScanNum = 0

    labels_dict = {1: 'GTV_P', 2: 'GTV_N'}

    planC = pc.load_nii_structure(nii_file_name, assocScanNum, planC, labels_dict)



### Export Scan, Structure and Dose to NifTi

    scanNiiFileName = r"\\path\to\Data\scan.nii.gz"

    scanNum = 0

    planC.scan[scanNum].save_nii(scanNiiFileName)

    

    structNiiFileName = r"\\path\to\Data\structure.nii.gz"

    structNum = 0

    planC.structure[structNum].save_nii(structNiiFileName, planC)    

    

    doseNiiFileName = r"\\path\to\Data\dose.nii.gz"

    doseNum = 0

    planC.dose[doseNum].save_nii(doseNiiFileName)

    



### visualize scan, dose and segmentation    

    scanNumList = [0]

    doseNumList = [0]

    numStructs = len(planC.structure)

    strNumList = np.arange(numStructs)

    displayMode = '2d' # '2d' or '3d'

    vectDict = {}

    viewer, scan_layer, dose_layer, struct_layer, dvf_layer = \

            vwr.showNapari(scanNumList, strNumList, doseNumList, vectDict, planC, displayMode)

            



### Compute DVH-based metrics

    from cerr import dvh

    structNum = 0

    doseNum = 0

    dosesV, volsV, isErr = dvh.getDVH(structNum, doseNum, planC)

    binWidth = 0.025

    doseBinsV,volHistV = dvh.doseHist(dosesV, volsV, binWidth)

    percent = 70

    dvh.MOHx(doseBinsV,volHistV,percent)