A plugin to lazily load multiscale whole-slide images with openslide and dask
An experimental plugin to lazily load multiscale whole-slide tiff images with openslide and dask.
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
openslideare also avaiable via
conda install -c sdvillal openslide-python
Step 2.) Install
pip install napari-lazy-openslide
$ 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
CAMELYON17 datasets, which can be
OpenSlideStore with Zarr and Dask¶
OpenSlideStore class wraps an
openslide.OpenSlide object as a valid Zarr store.
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"]["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[y_start:y_end, x_start:x_end]
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.
If you encounter any problems, please file an issue along with a detailed description.
- 19 May 2022
- 14 July 2020
- Information not submitted
- Stars: 24
- Forks: 4
- Issues + PRs: 1