napari-lazy-openslide 0.3.0

A plugin to lazily load multiscale whole-slide images with openslide and dask

napari-lazy-openslide

An experimental plugin to lazily load multiscale whole-slide tiff images with openslide and dask.

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This napari plugin was generated with Cookiecutter using with @napari's cookiecutter-napari-plugin template.

Installation

Step 1.) Make sure you have OpenSlide installed. Download instructions here.

NOTE: Installation on macOS is easiest via Homebrew: brew install openslide. Up-to-date and multiplatform binaries for openslide are also avaiable via conda: conda install -c sdvillal openslide-python

Step 2.) Install napari-lazy-openslide via pip:

pip install napari-lazy-openslide

Usage

Napari plugin

$ napari tumor_004.tif

By installing this package via pip, the plugin should be recognized by napari. The plugin attempts to read image formats recognized by openslide that are multiscale (openslide.OpenSlide.level_count > 1).

It should be noted that napari-lazy-openslide is experimental and has primarily been tested with CAMELYON16 and CAMELYON17 datasets, which can be downloaded here.

Using OpenSlideStore with Zarr and Dask

The OpenSlideStore class wraps an openslide.OpenSlide object as a valid Zarr store. The underlying openslide image pyramid is translated to the Zarr multiscales extension, where each level of the pyramid is a separate 3D zarr.Array with shape (y, x, 4).

import dask.array as da
import zarr

from napari_lazy_openslide import OpenSlideStore

store = OpenSlideStore('tumor_004.tif')
grp = zarr.open(store, mode="r")

# The OpenSlideStore implements the multiscales extension
# https://forum.image.sc/t/multiscale-arrays-v0-1/37930
datasets = grp.attrs["multiscales"][0]["datasets"]

pyramid = [grp.get(d["path"]) for d in datasets]
print(pyramid)
# [
#   <zarr.core.Array '/0' (23705, 29879, 4) uint8 read-only>,
#   <zarr.core.Array '/1' (5926, 7469, 4) uint8 read-only>,
#   <zarr.core.Array '/2' (2963, 3734, 4) uint8 read-only>,
# ]

pyramid = [da.from_zarr(store, component=d["path"]) for d in datasets]
print(pyramid)
# [
#   dask.array<from-zarr, shape=(23705, 29879, 4), dtype=uint8, chunksize=(512, 512, 4), chunktype=numpy.ndarray>,
#   dask.array<from-zarr, shape=(5926, 7469, 4), dtype=uint8, chunksize=(512, 512, 4), chunktype=numpy.ndarray>,
#   dask.array<from-zarr, shape=(2963, 3734, 4), dtype=uint8, chunksize=(512, 512, 4), chunktype=numpy.ndarray>,
# ]

# Now you can use numpy-like indexing with openslide, reading data into memory lazily!
low_res = pyramid[-1][:]
region = pyramid[0][y_start:y_end, x_start:x_end]

Contributing

Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.

Issues

If you encounter any problems, please file an issue along with a detailed description.