Original objects

We differentiate between the original and derived DICOM objects in the IDC portal and discussions of the IDC-hosted data. By Original objects we mean DICOM objects that are produced by image acquisition equipment - MR, CT, or PET images fall into this category. By Derived objects we mean those objects that were generated by means of analysis or annotation of the original objects. Those objects can contain, for example, volumetric segmentations of the structures in the original images, or quantitative measurements of the objects in the image.

Original objects

Most of the images stored on IDC are saved as objects that store individual slices of the image in separate instances of a series, with the image stored in the PixelData attribute.

As of production release, IDC contains both radiology and digital pathology images. The following publication can serve as a good introduction into the use of DICOM for digital pathology.

Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018). PubMed

Open source libraries such as DCMTK, GDCM, ITK, and pydicom can be used to parse such files and load pixel data of the individual slices. Recovering geometry of the individual slices (spatial location and resolution) and reconstruction of the individual slices into a volume requires some extra consideration.

You can use one of the existing tools to reconstruct image volume instead of implementing sorting of the slices on your own:

dcm2niix: command-line tool to convert neuroimaging data from the DICOM format to the NIfTI format
Plastimatch: open source software for image computation, which includes command-line file format converter
dicom2nifti: python library providing API and command-line tools for converting DICOM images into NIfTI format
SimpleITK: python interface to the Insight Toolkit, includes DICOM read functionality.

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Original objects

Most of the images stored on IDC are saved as objects that store individual slices of the image in separate instances of a series, with the image stored in the PixelData attribute.

As of production release, IDC contains both radiology and digital pathology images. The following publication can serve as a good introduction into the use of DICOM for digital pathology.

Herrmann, M. D., Clunie, D. A., Fedorov, A., Doyle, S. W., Pieper, S., Klepeis, V., Le, L. P., Mutter, G. L., Milstone, D. S., Schultz, T. J., Kikinis, R., Kotecha, G. K., Hwang, D. H., Andriole, K. P., John Lafrate, A., Brink, J. A., Boland, G. W., Dreyer, K. J., Michalski, M., Golden, J. A., Louis, D. N. & Lennerz, J. K. Implementing the DICOM standard for digital pathology. J. Pathol. Inform. 9, 37 (2018). PubMed

You can use one of the existing tools to reconstruct image volume instead of implementing sorting of the slices on your own:

dcm2niix: command-line tool to convert neuroimaging data from the DICOM format to the NIfTI format

Plastimatch: open source software for image computation, which includes command-line file format converter

dicom2nifti: python library providing API and command-line tools for converting DICOM images into NIfTI format

SimpleITK: python interface to the Insight Toolkit, includes DICOM read functionality.