# 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 Data_Curation_and_Predictions_to_RT
## Data_Curation_and_Predictions_to_RT has three main parts
### 1) Identify RT structures and names in multiple patients
### 2) Creating associations file and turning dicom into nifti/numpy files
### 3) Turning predictions into RT structures
#### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!
##### Please consider using the .write_parallel if you have many patients
##### Ring update allows for multiple rings to be represented correctly
Various utilities created to help with the interpretation of dicom images/RT Structures
RT Structure and dicom conversion to numpy arrays
This code is designed to receive an input path to a folder which contains both dicom images and a single RT structure file
For example, assume a folder exists with dicom files and an RT structure located at 'C:\users\brianmanderson\Patient_1\CT1\' with the roi 'Liver'
Assume there are 100 images, the generated data will be:
Dicom_Image.ArrayDicom is the image numpy array in the format [# images, rows, cols]
You can then call it to return a mask based on contour names called DicomImage.get_mask(), this takes in a list of Contour Names
You can see the available contour names with
Example:
from DicomRTTool import DicomReaderWriter
Dicom_reader = DicomReaderWriter(get_images_mask=False)
path = 'C:\users\brianmanderson\Patients\'
Dicom_reader.down_folder(path)
# See all rois in the folders
for roi in Dicom_reader.all_rois:
print(roi)
Contour_Names = ['Liver']
associations = {'Liver_BMA_Program4':'Liver','Liver':'Liver'}
path = 'C:\users\brianmanderson\Patients\Patient_1\CT_1\'
Dicom_reader = DicomReaderWriter(get_images_mask=True, Contour_Names=Contour_Names, associations=associations)
Dicom_reader.Make_Contour_From_directory(path)
image = DicomImage.ArrayDicom
mask = DicomImage.mask
pred = np.zeros([mask.shape[0],mask.shape[1],mask.shape[2],2]) # prediction needs to be [# images, rows, cols, # classes]
pred[:,200:300,200:300,1] = 1
output_path= os.path.join('.','Output')
Dicom_reader.with_annotations(pred,output_path,ROI_Names=['test'])
'''
Write the images and annotations as niftii files in parallel!
'''
Dicom_Reader.write_parallel(out_path=export_path,excel_file=os.path.join('.','MRN_Path_To_Iteration.xlsx'))
Raw data
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"description": "# This code provides functionality for turning dicom images and RT structures into nifti files as well as turning prediction masks back into RT structures\n# Installation guide\n pip install DicomRTTool\n## Highly recommend to go through the jupyter notebook Data_Curation_and_Predictions_to_RT\n## Data_Curation_and_Predictions_to_RT has three main parts\n### 1) Identify RT structures and names in multiple patients\n### 2) Creating associations file and turning dicom into nifti/numpy files\n### 3) Turning predictions into RT structures\n#### If you find this code useful, please provide a reference to my github page for others www.github.com/brianmanderson , thank you!\n##### Please consider using the .write_parallel if you have many patients\n##### Ring update allows for multiple rings to be represented correctly\n\n\n\nVarious utilities created to help with the interpretation of dicom images/RT Structures\n\nRT Structure and dicom conversion to numpy arrays\n\nThis code is designed to receive an input path to a folder which contains both dicom images and a single RT structure file\n\nFor example, assume a folder exists with dicom files and an RT structure located at 'C:\\users\\brianmanderson\\Patient_1\\CT1\\' with the roi 'Liver'\n\nAssume there are 100 images, the generated data will be:\nDicom_Image.ArrayDicom is the image numpy array in the format [# images, rows, cols]\n\nYou can then call it to return a mask based on contour names called DicomImage.get_mask(), this takes in a list of Contour Names\n\nYou can see the available contour names with\n\nExample:\n\n from DicomRTTool import DicomReaderWriter\n Dicom_reader = DicomReaderWriter(get_images_mask=False)\n path = 'C:\\users\\brianmanderson\\Patients\\'\n Dicom_reader.down_folder(path)\n # See all rois in the folders\n for roi in Dicom_reader.all_rois:\n print(roi)\n\n\n Contour_Names = ['Liver']\n associations = {'Liver_BMA_Program4':'Liver','Liver':'Liver'}\n path = 'C:\\users\\brianmanderson\\Patients\\Patient_1\\CT_1\\'\n Dicom_reader = DicomReaderWriter(get_images_mask=True, Contour_Names=Contour_Names, associations=associations)\n\n Dicom_reader.Make_Contour_From_directory(path)\n image = DicomImage.ArrayDicom\n mask = DicomImage.mask\n\n pred = np.zeros([mask.shape[0],mask.shape[1],mask.shape[2],2]) # prediction needs to be [# images, rows, cols, # classes]\n pred[:,200:300,200:300,1] = 1\n\n output_path= os.path.join('.','Output')\n Dicom_reader.with_annotations(pred,output_path,ROI_Names=['test'])\n\n '''\n Write the images and annotations as niftii files in parallel!\n '''\n Dicom_Reader.write_parallel(out_path=export_path,excel_file=os.path.join('.','MRN_Path_To_Iteration.xlsx'))\n\n\n\n",
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