Documentation of the Highdicom Package

highdicom is a pure Python package built on top of pydicom to provide a higher-level application programming interface (API) for working with DICOM files. Its focus is on common operations required for machine learning, computer vision, and other similar computational analyses. Broadly speaking, the package helps with three types of task:

  1. Reading existing DICOM image files of a wide variety of modalities (covering radiology, pathology, and more) and selecting and formatting its frames for computational analysis. This includes considerations such as spatial arrangements of frames, and application of pixel transforms, which are not handled by pydicom.

  2. Storing image-derived information, for example from computational analyses or human annotation, in derived DICOM objects for communication and storage. This includes:

  • Annotations

  • Parametric Map images

  • Segmentation images

  • Structured Report documents (containing numerical results, qualitative evaluations, and/or vector graphic annotations)

  • Secondary Capture images

  • Key Object Selection documents

  • Legacy Converted Enhanced CT/PET/MR images (e.g., for single frame to multi-frame conversion)

  • Softcopy Presentation State instances (including Grayscale, Color, and Pseudo-Color)

  1. Reading existing derived DICOM files of the above types and filtering and accessing the information contained within them.

For new users looking to get an overview of the library’s capabilities and perform basic tasks, we recommend starting with the Quick Start page. For a detailed introduction to many of the library’s capabilities, see the rest of the User Guide. Documentation of all classes and functions may be found in the API Documentation.

For questions, suggestions, or to report bugs, please use the issue tracker on our GitHub repository.

Contents:

Indices and tables