cuCIM

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

Quick Start

Install cuCIM

pip install cucim

# Install dependencies for `cucim.skimage` (assuming that CUDA 11.0 is used for CuPy)
pip install scipy scikit-image cupy-cuda110==9.0.0b3

Jupyter Notebooks

Please check out our Welcome notebook.

Open Image

from cucim import CuImage
img = CuImage('image.tif')

See Metadata

import json
print(img.is_loaded)        # True if image data is loaded & available.
print(img.device)           # A device type.
print(img.ndim)             # The number of dimensions.
print(img.dims)             # A string containing a list of dimensions being requested.
print(img.shape)            # A tuple of dimension sizes (in the order of `dims`).
print(img.size('XYC'))      # Returns size as a tuple for the given dimension order.
print(img.dtype)            # The data type of the image.
print(img.channel_names)    # A channel name list.
print(img.spacing())        # Returns physical size in tuple.
print(img.spacing_units())  # Units for each spacing element (size is same with `ndim`).
print(img.origin)           # Physical location of (0, 0, 0) (size is always 3).
print(img.direction)        # Direction cosines (size is always 3x3).
print(img.coord_sys)        # Coordinate frame in which the direction cosines are
                            # measured. Available Coordinate frame is not finalized yet.

# Returns a set of associated image names.
print(img.associated_images)
# Returns a dict that includes resolution information.
print(json.dumps(img.resolutions, indent=2))
# A metadata object as `dict`
print(json.dumps(img.metadata, indent=2))
# A raw metadata string.
print(img.raw_metadata)

Read Region

# Install matplotlib (`pip install matplotlib`) if not installed before.
from matplotlib import pyplot as plt
def visualize(image):
    dpi = 80.0
    height, width, _ = image.shape
    plt.figure(figsize=(width / dpi, height / dpi))
    plt.axis('off')
    plt.imshow(image)

import numpy as np

# Read whole slide at the highest resolution
resolutions = img.resolutions
level_count = resolutions['level_count']  # level: 0 ~ (level_count - 1)

# Note: ‘level’ is at 3rd parameter (OpenSlide has it at 2nd parameter)
#   `location` is level-0 based coordinates (using the level-0 reference frame)
#   If `size` is not specified, size would be (width, height) of the image at the specified `level`.
region = img.read_region(location=(5000, 5000), size=(512, 512), level=0)

visualize(region)
#from PIL import Image
#Image.fromarray(np.asarray(region))

Using scikit-image API

Import cucim.skimage instead of skimage.

# The following code is modified from https://scikit-image.org/docs/dev/auto_examples/color_exposure/plot_ihc_color_separation.html#sphx-glr-auto-examples-color-exposure-plot-ihc-color-separation-py
#
import cupy as cp  # modified from: `import numpy as np`
import matplotlib.pyplot as plt

# from skimage import data
from cucim.skimage.color import rgb2hed, hed2rgb  # modified from: `from skimage.color import rgb2hed, hed2rgb`

# Example IHC image
ihc_rgb = cp.asarray(region)  # modified from: `ihc_rgb = data.immunohistochemistry()`

# Separate the stains from the IHC image
ihc_hed = rgb2hed(ihc_rgb)

# Create an RGB image for each of the stains
null = cp.zeros_like(ihc_hed[:, :, 0])  # np -> cp
ihc_h = hed2rgb(cp.stack((ihc_hed[:, :, 0], null, null), axis=-1))  # np -> cp
ihc_e = hed2rgb(cp.stack((null, ihc_hed[:, :, 1], null), axis=-1))  # np -> cp
ihc_d = hed2rgb(cp.stack((null, null, ihc_hed[:, :, 2]), axis=-1))  # np -> cp

# Display
fig, axes = plt.subplots(2, 2, figsize=(7, 6), sharex=True, sharey=True)
ax = axes.ravel()

ax[0].imshow(ihc_rgb.get())  # appended `.get()`
ax[0].set_title("Original image")

ax[1].imshow(ihc_h.get())  # appended `.get()`
ax[1].set_title("Hematoxylin")

ax[2].imshow(ihc_e.get())  # appended `.get()`
ax[2].set_title("Eosin")

ax[3].imshow(ihc_d.get())  # appended `.get()`
ax[3].set_title("DAB")

for a in ax.ravel():
    a.axis('off')

fig.tight_layout()

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) 2020-2021, NVIDIA CORPORATION.

Changelog

0.19.0 (2021-04-19)

• The first release of cuClaraImage + cupyimg as a single project cuCIM.
  • cucim.skimage package is added from cupyimg.
  • CuPy (>=9.0.0b3), scipy, scikit-image is required to use cuCIM's scikit-image-compatible API.

0.18.3 (2021-04-16)

• Fix memory leaks that occur when reading completely out-of-boundary regions.

0.18.2 (2021-03-29)

• Use the white background only for Philips TIFF file.
  • Generic TIFF file would have the black background by default.
• Fix upside-downed image for TIFF file if the image is not RGB & tiled image with JPEG/Deflate-compressed tiles.
  • Use slow path if the image is not RGB & tiled image with JPEG/Deflate-compressed tiles.
    • Show an error message if the out-of-boundary cases are requested with the slow path.
    • ValueError: Cannot handle the out-of-boundary cases for a non-RGB image or a non-Jpeg/Deflate-compressed image.

0.18.1 (2021-03-17)

• Disable using cuFile
  • Remove warning messages when libcufile.so is not available.
    • [warning] CuFileDriver cannot be open. Falling back to use POSIX file IO APIs.

0.18.0 (2021-03-16)

• First release on PyPI with only cuClaraImage features.
• The namespace of the project is changed from cuimage to cucim and project name is now cuCIM
• Support Deflate(zlib) compression in Generic TIFF Format.
  • libdeflate library is used to decode the deflate-compressed data.