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medical_imaging

Medical Imaging Processing and Inference Operators

This set of operators accelerate the development of medical imaging AI inference application with DICOM imaging network integration by providing the following,

  • application classes to automate the inference with MONAI Bundle as well as normal TorchScript models
  • classes to load supported AI model from files to detected devices, GPU or CPU
  • classes to parse runtime options and well-known environment variables
  • DICOM study parsing and selection classes, as well as DICOM instance to volume image conversion
  • DICOM instance writers to encapsulate AI inference results in these DICOM OID,
  • DICOM Segmentation
  • DICOM Basic Text Structured Report
  • DICOM Encapsulated PDF
  • Surface mesh generation and storage in STL format
  • Visualization with Clara-Viz integration, as needed

Requirements

This set of operators depends on Holoscan SDK Python package, as well as directly on the following, - highdicom - monai - nibabel - numpy - numpy-stl - Pillow - pydicom - PyPDF2 - scikit-image - SimpleITK - torch - trimesh - typeguard

Notices

Many of this set of operators are Derivative Works of MONAI Deploy App SDK under its Apache-2.0 license, and Nvidia employees have been the main contributors to MONAI Deploy App SDK.

The dependency packages' licences can be viewed at their respective links as shown in the above section.