Loading contours stored in DICOM format (RTstructure).

Segmentation mask is stored in DICOMs in as a set of plain contours. These contours are nothing but a 2D curves defined by set of coordinates in 3D space. Specifically these coordinates are in physical space not in voxel space. Every single RTStructure might contain contours for multiple masks (e.g. Brain, Left_Eye, GTV etc.).

dicom-csv contains several functions to collect contours stored in RTstructures and move them into image’s voxel space.

Crawling images folder

join_tree is the main function that collects the DICOM files’ metadata:

from dicom_csv import join_tree

df = join_tree(path, relative=False, verbose=False)

Select rows related to RTStructures

collect_rtstruct is the function which selects rows related to RTstructures and copies additional metadata from corresponding images (RTstructure itself does not contain information about spatial orientation, it only contains coordinates and link to corresponding DICOM image):

from dicom_csv.rtstructs.csv import collect_rtstructs

df_rtstructs = collect_rtstructs(df)

Extract contours coordinates from RTStructure

contours_dict extract all coordinates from a single row of df_rtstructs:

from dicom_csv.rtstruct.contour import read_rtstruct

contours_dict = read_rtstruct(df_rtstructs.iloc[0])

It outputs python dictionary with keys - names of the contours and values - n x 3 nd.arrays of coordinates in physical space.

Move contours to voxel space

Finally, contours_image_dict moves these coordinates into voxel space (basis change transformation, essentially rotation, translation and stretching):

from dicom_csv.rtstruct.contour import contours_image_dict

contours_image_dict = contours_to_image(df_rtstructs.iloc[0], contours_dict)

Resulting dictionary contains all contours stored in corresponding RTStructure.

Contours to masks

TODO