Add organoidnet dataset (#318)

torch_em/data/datasets/light_microscopy/__init__.py

@@ -11,5 +11,6 @@
  get_neurips_cellseg_unsupervised_loader, get_neurips_cellseg_unsupervised_dataset
 )
 from .orgasegment import get_orgasegment_dataset, get_orgasegment_loader
+from .organoidnet import get_organoidnet_dataset, get_organoidnet_loader
 from .plantseg import get_plantseg_loader, get_plantseg_dataset
 from .tissuenet import get_tissuenet_loader, get_tissuenet_dataset

torch_em/data/datasets/light_microscopy/organoidnet.py

@@ -0,0 +1,109 @@
+import os
+import shutil
+import zipfile
+
+from glob import glob
+from typing import Tuple, Union
+
+import torch_em
+
+from .. import util
+
+
+URL = "https://zenodo.org/records/10643410/files/OrganoIDNetData.zip?download=1"
+CHECKSUM = "3cd9239bf74bda096ecb5b7bdb95f800c7fa30b9937f9aba6ddf98d754cbfa3d"
+
+
+def get_organoidnet_data(path, split, download):
+ splits = ["Training", "Validation", "Test"]
+ assert split in splits
+
+ os.makedirs(path, exist_ok=True)
+
+ data_dir = os.path.join(path, split)
+ if os.path.exists(data_dir):
+ return data_dir
+
+ # Download and extraction.
+ zip_path = os.path.join(path, "OrganoIDNetData.zip")
+ util.download_source(path=zip_path, url=URL, download=download, checksum=CHECKSUM)
+
+ # Only "Training", "Test", "Validation" from the zip are relevant and need to be extracted.
+ # They are in "/OrganoIDNetData/Dataset/"
+ prefix = "OrganoIDNetData/Dataset/"
+ for dl_split in splits:
+
+ dl_prefix = prefix + dl_split
+
+ with zipfile.ZipFile(zip_path) as archive:
+ for ff in archive.namelist():
+ if ff.startswith(dl_prefix):
+ archive.extract(ff, path)
+
+ for dl_split in splits:
+ shutil.move(
+ os.path.join(path, "OrganoIDNetData/Dataset", dl_split),
+ os.path.join(path, dl_split)
+ )
+
+ assert os.path.exists(data_dir)
+
+ os.remove(zip_path)
+ return data_dir
+
+
+def _get_data_paths(path, split, download):
+ data_dir = get_organoidnet_data(path=path, split=split, download=download)
+
+ image_paths = sorted(glob(os.path.join(data_dir, "Images", "*.tif")))
+ label_paths = sorted(glob(os.path.join(data_dir, "Masks", "*.tif")))
+
+ return image_paths, label_paths
+
+
+def get_organoidnet_dataset(
+ path: Union[os.PathLike, str],
+ split: str,
+ patch_shape: Tuple[int, int],
+ download: bool = False,
+ **kwargs
+):
+ """Dataset for the segmentation of panceratic organoids.
+
+ This dataset is from the publication https://doi.org/10.1007/s13402-024-00958-2.
+ Please cite it if you use this dataset for a publication.
+ """
+ image_paths, label_paths = _get_data_paths(path=path, split=split, download=download)
+
+ return torch_em.default_segmentation_dataset(
+ raw_paths=image_paths,
+ raw_key=None,
+ label_paths=label_paths,
+ label_key=None,
+ patch_shape=patch_shape,
+ is_seg_dataset=False,
+ **kwargs
+ )
+
+
+def get_organoidnet_loader(
+ path: Union[os.PathLike, str],
+ split: str,
+ patch_shape: Tuple[int, int],
+ batch_size: int,
+ download: bool = False,
+ **kwargs
+):
+ """Dataloader for the segmentation of pancreatic organoids in brightfield images.
+ See `get_organoidnet_dataset` for details.
+ """
+ ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+ dataset = get_organoidnet_dataset(
+ path=path,
+ split=split,
+ patch_shape=patch_shape,
+ download=download,
+ **ds_kwargs
+ )
+ loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+ return loader