cuCIM

RAPIDS cuCIM (pronounced "koo-sim", see here) is an open-source, accelerated computer vision and image processing software library for multidimensional images used in biomedical, geospatial, material and life science, and remote sensing use cases.

cuCIM offers:

• Enhanced Image Processing Capabilities for large and n-dimensional tag image file format (TIFF) files
• Accelerated performance through Graphics Processing Unit (GPU)-based image processing and computer vision primitives
• A Straightforward Pythonic Interface with Matching Application Programming Interface (API) for Openslide

cuCIM supports the following formats:

• Aperio ScanScope Virtual Slide (SVS)
• Philips TIFF
• Generic Tiled, Multi-resolution RGB TIFF files with the following compression schemes:
  • No Compression
  • JPEG
  • JPEG2000
  • Lempel-Ziv-Welch (LZW)
  • Deflate

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

• GTC 2022 Accelerating Storage IO to GPUs with Magnum IO [S41347]
  • cuCIM's GDS API examples: https://github.com/NVIDIA/MagnumIO/tree/main/gds/readers/cucim-gds
• SciPy 2021 cuCIM - A GPU image I/O and processing library
  • video
• GTC 2021 cuCIM: A GPU Image I/O and Processing Toolkit [S32194]
  • video

Developer Page

Blogs - Enhanced Image Analysis with Multidimensional Image Processing - Accelerating Scikit-Image API with cuCIM: n-Dimensional Image Processing and IO on GPUs - Accelerating Digital Pathology Pipelines with NVIDIA Clara™ Deploy

Webinars

• cuCIM: a GPU Image IO and Processing Library

Documentation

Release notes are available on our wiki page.

Install cuCIM

Conda

Conda (stable)

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

<CUDA version> should be 12.0+ (e.g., 12.0, etc.)

Conda (nightlies)

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

<CUDA version> should be 12.0+ (e.g., 12.0, etc.)

PyPI

Install for CUDA 12:

pip install cucim-cu12

Install for CUDA 13:

pip install cucim-cu13

Notebooks

Please check out our Welcome notebook (NBViewer)

Downloading sample images

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}

cuslide2 Plugin (Experimental)

cuCIM includes an experimental cuslide2 plugin that uses nvImageCodec for GPU-accelerated TIFF decoding. This plugin provides enhanced performance for batch ROI decoding operations.

Enabling cuslide2

To enable the cuslide2 plugin, set the ENABLE_CUSLIDE2 environment variable:

ENABLE_CUSLIDE2=1 python your_script.py

Test Scripts

cuCIM provides test scripts to verify cuslide2 functionality with different TIFF formats:

Aperio SVS files:

# Download a sample SVS file and run the test
python scripts/test_aperio_svs.py --download

# Or test with your own SVS file
ENABLE_CUSLIDE2=1 python scripts/test_aperio_svs.py /path/to/your/file.svs

Philips TIFF files:

# Test with a Philips TIFF file
ENABLE_CUSLIDE2=1 python scripts/test_philips_tiff.py /path/to/your/philips.tiff

# List available test options
python scripts/test_philips_tiff.py --help

Build/Install from Source

See build instructions.

Contributing Guide

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.

Acknowledgments

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.

License

Apache-2.0 License (see LICENSE file).

Copyright (c) 2020-2026, NVIDIA CORPORATION.