Add DynamicNuclearNet data - from DeepCell

scripts/datasets/check_dynamicnuclearnet.py

@@ -0,0 +1,20 @@
+from torch_em.util.debug import check_loader
+from torch_em.data.datasets import get_dynamicnuclearnet_loader
+
+
+DYNAMICNUCLEARNET_ROOT = "/home/anwai/data/deepcell/"
+
+
+# NOTE: the DynamicNuclearNet data cannot be downloaded automatically.
+# you need to download it yourself from https://datasets.deepcell.org/data
+def check_dynamicnuclearnet():
+    # set this path to where you have downloaded the dynamicnuclearnet data
+    loader = get_dynamicnuclearnet_loader(
+        DYNAMICNUCLEARNET_ROOT, "train",
+        patch_shape=(512, 512), batch_size=2, download=True
+    )
+    check_loader(loader, 10, instance_labels=True, rgb=False)
+
+
+if __name__ == "__main__":
+    check_dynamicnuclearnet()

torch_em/data/datasets/__init__.py

@@ -6,6 +6,7 @@
 from .ctc import get_ctc_segmentation_loader, get_ctc_segmentation_dataset
 from .deepbacs import get_deepbacs_loader, get_deepbacs_dataset
 from .dsb import get_dsb_loader, get_dsb_dataset
+from .dynamicnuclearnet import get_dynamicnuclearnet_loader, get_dynamicnuclearnet_dataset
 from .hpa import get_hpa_segmentation_loader, get_hpa_segmentation_dataset
 from .isbi2012 import get_isbi_loader, get_isbi_dataset
 from .kasthuri import get_kasthuri_loader, get_kasthuri_dataset

torch_em/data/datasets/dynamicnuclearnet.py

@@ -0,0 +1,93 @@
+import os
+from tqdm import tqdm
+from glob import glob
+
+import z5py
+import numpy as np
+import pandas as pd
+
+import torch_em
+
+from . import util
+
+
+# Automatic download is currently not possible, because of authentication
+URL = None  # TODO: here - https://datasets.deepcell.org/data
+
+
+def _create_split(path, split):
+    split_file = os.path.join(path, "DynamicNuclearNet-segmentation-v1_0", f"{split}.npz")
+    split_folder = os.path.join(path, split)
+    os.makedirs(split_folder, exist_ok=True)
+    data = np.load(split_file, allow_pickle=True)
+
+    x, y = data["X"], data["y"]
+    metadata = data["meta"]
+    metadata = pd.DataFrame(metadata[1:], columns=metadata[0])
+
+    for i, (im, label) in tqdm(enumerate(zip(x, y)), total=len(x), desc=f"Creating files for {split}-split"):
+        out_path = os.path.join(split_folder, f"image_{i:04}.zarr")
+        image_channel = im[..., 0]
+        label_channel = label[..., 0]
+        chunks = image_channel.shape
+        with z5py.File(out_path, "a") as f:
+            f.create_dataset("raw", data=image_channel, compression="gzip", chunks=chunks)
+            f.create_dataset("labels", data=label_channel, compression="gzip", chunks=chunks)
+
+    os.remove(split_file)
+
+
+def _create_dataset(path, zip_path):
+    util.unzip(zip_path, path, remove=False)
+    splits = ["train", "val", "test"]
+    assert all(
+        [os.path.exists(os.path.join(path, "DynamicNuclearNet-segmentation-v1_0", f"{split}.npz")) for split in splits]
+    )
+    for split in splits:
+        _create_split(path, split)
+
+
+def get_dynamicnuclearnet_dataset(
+    path, split, patch_shape, download=False, **kwargs
+):
+    """Dataset for the segmentation of cell nuclei imaged with fluorescene microscopy.
+
+    This dataset is from the publication https://doi.org/10.1101/803205.
+    Please cite it if you use this dataset for a publication."""
+    splits = ["train", "val", "test"]
+    assert split in splits
+
+    # check if the dataset exists already
+    zip_path = os.path.join(path, "DynamicNuclearNet-segmentation-v1_0.zip")
+    if all([os.path.exists(os.path.join(path, split)) for split in splits]):  # yes it does
+        pass
+    elif os.path.exists(zip_path):  # no it does not, but we have the zip there and can unpack it
+        _create_dataset(path, zip_path)
+    else:
+        raise RuntimeError(
+            "We do not support automatic download for the dynamic nuclear net dataset yet."
+            f"Please download the dataset from https://datasets.deepcell.org/data and put it here: {zip_path}"
+        )
+
+    split_folder = os.path.join(path, split)
+    assert os.path.exists(split_folder)
+    data_path = glob(os.path.join(split_folder, "*.zarr"))
+    assert len(data_path) > 0
+
+    raw_key, label_key = "raw", "labels"
+
+    return torch_em.default_segmentation_dataset(
+        data_path, raw_key, data_path, label_key, patch_shape, is_seg_dataset=True, ndim=2, **kwargs
+    )
+
+
+def get_dynamicnuclearnet_loader(
+    path, split, patch_shape, batch_size, download, **kwargs
+):
+    """Dataloader for the segmentation of cell nuclei for 5 different cell lines in fluorescence microscopes.
+    See `get_dynamicnuclearnet_dataset` for details.
+"""
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_dynamicnuclearnet_dataset(path, split, patch_shape, download, **ds_kwargs)
+    loader = torch_em.get_data_loader(dataset, batch_size, **loader_kwargs)
+    return loader