https://github.com/brianmanderson/dicom_rt_and_images_to_mask

Tools to help with the conversion of DICOM images, RT Structures, and dose to useful Python objects. Essentially DICOM to NumPy and SimpleITK Images
https://github.com/brianmanderson/dicom_rt_and_images_to_mask

binary-masks deep-learning deeplearning dicom dicom-images dicomrt mask medical-image-processing medical-imaging nifti-images numpy python roi roi-segmentation rt-struct rt-structs rt-utils rtstruct

Last synced: 2 days ago
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Tools to help with the conversion of DICOM images, RT Structures, and dose to useful Python objects. Essentially DICOM to NumPy and SimpleITK Images

• Host: GitHub
• URL: https://github.com/brianmanderson/dicom_rt_and_images_to_mask
• Owner: brianmanderson
• License: gpl-3.0
• Created: 2019-04-08T17:24:49.000Z (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2024-12-03T17:10:19.000Z (about 2 months ago)
• Last Synced: 2025-01-20T00:06:28.537Z (2 days ago)
• Topics: binary-masks, deep-learning, deeplearning, dicom, dicom-images, dicomrt, mask, medical-image-processing, medical-imaging, nifti-images, numpy, python, roi, roi-segmentation, rt-struct, rt-structs, rt-utils, rtstruct
• Language: Python
• Homepage:
• Size: 73.9 MB
• Stars: 93
• Watchers: 4
• Forks: 29
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.txt

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README

        
# We're published! Please check out the Technical Note here: https://www.sciencedirect.com/science/article/abs/pii/S1879850021000485 and reference this work if you find it useful

### DOI:https://doi.org/10.1016/j.prro.2021.02.003

## This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures

## Installation guide

    pip install DicomRTTool

### Highly recommend to go through the jupyter notebook in the Examples folder and to read the Wiki

### Quick use guide

    from DicomRTTool.ReaderWriter import DicomReaderWriter, ROIAssociationClass

    Dicom_path = r'.some_path_to_dicom'

    Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True)

    Dicom_reader.walk_through_folders(Dicom_path) # This will parse through all DICOM present in the folder and subfolders

    all_rois = Dicom_reader.return_rois(print_rois=True) # Return a list of all rois present

    

    Contour_names = ['tumor'] # Define what rois you want

    associations = [ROIAssociationClass('tumor', ['tumor_mr', 'tumor_ct'])] # Any list of roi associations

    Dicom_reader.set_contour_names_and_assocations(contour_names=Contour_names, associations=associations)

    

    Dicom_reader.get_images_and_mask()

    

    image_numpy = Dicom_reader.ArrayDicom

    mask_numpy = Dicom_reader.mask

    image_sitk_handle = Dicom_reader.dicom_handle

    mask_sitk_handle = Dicom_reader.annotation_handle

### Other interesting additions

### Adding information to the Dicom_reader.series_instances_dictionary

    from DicomRTTool.ReaderWriter import Tag

    plan_pydicom_string_keys = {"MyNamedRTPlan": Tag((0x300a, 0x002))}

    image_sitk_string_keys = {"MyPatientName": "0010|0010"}

    Dicom_reader = DicomReaderWriter(description='Examples', arg_max=True, plan_pydicom_string_keys=plan_pydicom_string_keys, image_sitk_string_keys=image_sitk_string_keys)

    

##### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!

###### Ring update allows for multiple rings to be represented correctly

![multiple_rings.png](./Images/multiple_rings.png)

#### Works on oblique images for masks and predictions*