Loading patches/windows from masked regions of images with ZarrDataset

Import the “zarrdataset” package

import zarrdataset as zds
import zarr

Load data stored on S3 storage

# These are images from the Image Data Resource (IDR) 
# https://idr.openmicroscopy.org/ that are publicly available and were 
# converted to the OME-NGFF (Zarr) format by the OME group. More examples
# can be found at Public OME-Zarr data (Nov. 2020)
# https://www.openmicroscopy.org/2020/11/04/zarr-data.html

filenames = ["https://uk1s3.embassy.ebi.ac.uk/idr/zarr/v0.4/idr0073A/9798462.zarr"]

import random
import numpy as np

# For reproducibility
np.random.seed(478963)
random.seed(478965)

z_img = zarr.open(filenames[0], mode="r")
z_img["0"].info
Name/0
Typezarr.core.Array
Data typeuint8
Shape(1, 3, 1, 16433, 21115)
Chunk shape(1, 1, 1, 1024, 1024)
OrderC
Read-onlyTrue
CompressorBlosc(cname='lz4', clevel=5, shuffle=SHUFFLE, blocksize=0)
Store typezarr.storage.FSStore
No. bytes1040948385 (992.7M)
Chunks initialized0/1071
import matplotlib.pyplot as plt
plt.imshow(np.moveaxis(z_img["4"][0, :, 0], 0, -1))
plt.show()
../_images/d495d73aa08da19be90e594be5c54833cf2e178ae7cae10432fb766694918ab3.png

Define a mask from where patches will be extracted

mask = np.array([
    [0, 0, 0, 0, 0],
    [0, 0, 1, 0, 0],
    [0, 1, 0, 0, 0],
    [0, 0, 0, 0, 0],
    [0, 0, 0, 0, 1],
], dtype=bool)

ZarrDataset will match the size of the mask t the size of the image that is being sampled.

For that reason, it is not necessary for the mask to be of the same size of the image.

_, d, _, h, w = z_img["4"].shape
m_h, m_w = mask.shape

factor_h = h / m_h
factor_w = w / m_w

plt.imshow(np.moveaxis(z_img["4"][0, :, 0], 0, -1))

sampling_region = np.array([
    [0, 0],
    [0, factor_w],
    [factor_h, factor_w],
    [factor_h, 0],
    [0, 0]
])

for m_y, m_x in zip(*np.nonzero(mask)):
    offset_y = m_y * factor_h
    offset_x = m_x * factor_w
    plt.plot(sampling_region[:, 1] + offset_x,
             sampling_region[:, 0] + offset_y)

plt.show()
../_images/581d6cf1ef0478d96c82490e1c35a2803eb79b5caecadbb41767db6d594577b2.png

Extract patches of size 512x512 pixels from masked regiosn of a Whole Slide Image (WSI)

Sample the image uniformly in a squared grid pattern using a PatchSampler

patch_size = dict(Y=512, X=512)
patch_sampler = zds.PatchSampler(patch_size=patch_size)

Use the ZarrDataset class to enable extraction of samples from masked regions by specifying two modalities: images, and masks.

Enable sampling patched from random locations with shuffle=True

image_specs = zds.ImagesDatasetSpecs(
  filenames=filenames,
  data_group="0",
  source_axes="TCZYX",
)

# Use the MasksDatasetSpecs to add the specifications of the masks.
# Filenames can receive different types of variables, in this case a list with a single mask for the only image in image_specs.
masks_specs = zds.MasksDatasetSpecs(
  filenames=[mask],
  source_axes="YX",
)

my_dataset = zds.ZarrDataset([image_specs, masks_specs],
                             patch_sampler=patch_sampler,
                             draw_same_chunk=False,
                             shuffle=True)

ds_iterator = iter(my_dataset)

sample = next(ds_iterator)
type(sample), sample.shape, sample.dtype

(numpy.ndarray, (1, 3, 1, 512, 512), dtype('uint8'))

plt.imshow(np.moveaxis(sample[0, :, 0], 0, -1))
plt.show()
../_images/3be027298b03a07f90dfb75295e5c9397e2b327d1555bbb30091d66e0f9a8530.png 
samples = []
for i, sample in enumerate(my_dataset):
    samples.append(np.pad(np.moveaxis(sample[0, :, 0], 0, -1),((1, 1), (1, 1), (0, 0))))

    # Obtain only 5 samples
    if i >= 4:
        break

grid_samples = np.hstack(samples)

plt.imshow(grid_samples)
plt.show()
../_images/b5fc7c1e8c771ba15f40e2bc046ece21d212137494de55adb8827adb748fae85.png