RAPIDS cuCIM is an extensible toolkit designed to provide GPU accelerated I/O, computer vision & image processing primitives for N-Dimensional images with a focus on biomedical imaging.

NOTE: For the latest stable README.md ensure you are on the main branch.

• GTC 2021 cuCIM: A GPU Image I/O and Processing Toolkit [S32194]
  • video
• SciPy 2021 cuCIM - A GPU image I/O and processing library
  • video

Release notes are available on our wiki page.

conda create -n cucim -c rapidsai -c conda-forge cucim cudatoolkit=<CUDA version>

<CUDA version> should be 11.0+ (e.g., 11.0, 11.2, etc.)

conda create -n cucim -c rapidsai-nightly -c conda-forge cucim cudatoolkit=<CUDA version>

<CUDA version> should be 11.0+ (e.g., 11.0, 11.2, etc)

Please check out our Welcome notebook (NBViewer)

To download images used in the notebooks, please execute the following commands from the repository root folder to copy sample input images into notebooks/input folder:

(You will need Docker installed in your system)

./run download_testdata

or

mkdir -p notebooks/input
tmp_id=$(docker create gigony/svs-testdata:little-big)
docker cp $tmp_id:/input notebooks
docker rm -v ${tmp_id}

See build instructions.

Contributions to cuCIM are more than welcome! Please review the CONTRIBUTING.md file for information on how to contribute code and issues to the project.

Without awesome third-party open source software, this project wouldn't exist.

Please find LICENSE-3rdparty.md to see which third-party open source software is used in this project.

Apache-2.0 License (see LICENSE file).

Copyright (c) 2020-2021, NVIDIA CORPORATION.