Add MicroUSP dataset

scripts/datasets/medical/check_micro_usp.py

@@ -0,0 +1,24 @@
+from torch_em.data import MinInstanceSampler
+from torch_em.util.debug import check_loader
+from torch_em.data.datasets.medical import get_micro_usp_loader
+
+
+ROOT = "/media/anwai/ANWAI/data/micro-usp"
+
+
+def check_micro_usp():
+    loader = get_micro_usp_loader(
+        path=ROOT,
+        patch_shape=(1, 512, 512),
+        batch_size=2,
+        split="train",
+        resize_inputs=True,
+        download=True,
+        sampler=MinInstanceSampler(),
+    )
+
+    check_loader(loader, 8)
+
+
+if __name__ == "__main__":
+    check_micro_usp()

torch_em/data/datasets/medical/__init__.py

@@ -6,9 +6,10 @@
 from .feta24 import get_feta24_dataset, get_feta24_loader
 from .idrid import get_idrid_dataset, get_idrid_loader
 from .jnuifm import get_jnuifm_dataset, get_jnuifm_loader
+from .micro_usp import get_micro_usp_dataset, get_micro_usp_loader
 from .montgomery import get_montgomery_dataset, get_montgomery_loader
-from .oimhs import get_oimhs_dataset, get_oimhs_loader
 from .msd import get_msd_dataset, get_msd_loader
+from .oimhs import get_oimhs_dataset, get_oimhs_loader
 from .osic_pulmofib import get_osic_pulmofib_dataset, get_osic_pulmofib_loader
 from .papila import get_papila_dataset, get_papila_loader
 from .plethora import get_plethora_dataset, get_plethora_loader

torch_em/data/datasets/medical/micro_usp.py

@@ -0,0 +1,89 @@
+import os
+from glob import glob
+from pathlib import Path
+from natsort import natsorted
+from typing import Union, Tuple
+
+import torch_em
+
+from .. import util
+
+
+URL = "https://zenodo.org/records/10475293/files/Micro_Ultrasound_Prostate_Segmentation_Dataset.zip?"
+CHECKSUM = "031645dc30948314e379d0a0a7d54bad1cd4e1f3f918b77455d69810aa05dce3"
+
+
+def get_micro_usp_data(path, download):
+    os.makedirs(path, exist_ok=True)
+
+    fname = Path(URL).stem
+    data_dir = os.path.join(path, fname)
+    if os.path.exists(data_dir):
+        return data_dir
+
+    zip_path = os.path.join(path, f"{fname}.zip")
+    util.download_source(path=zip_path, url=URL, download=download, checksum=CHECKSUM)
+    util.unzip(zip_path=zip_path, dst=path)
+
+    return data_dir
+
+
+def _get_micro_usp_paths(path, split, download):
+    data_dir = get_micro_usp_data(path=path, download=download)
+
+    image_paths = natsorted(glob(os.path.join(data_dir, split, "micro_ultrasound_scans", "*.nii.gz")))
+    gt_paths = natsorted(glob(os.path.join(data_dir, split, "expert_annotations", "*.nii.gz")))
+
+    return image_paths, gt_paths
+
+
+def get_micro_usp_dataset(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, ...],
+    split: str,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataset for segmentation of prostate in micro-ultrasound scans.
+
+    This dataset is from Jiang et al. - https://doi.org/10.1016/j.compmedimag.2024.102326.
+    Please cite it if you use this dataset for a publication.
+    """
+    image_paths, gt_paths = _get_micro_usp_paths(path=path, split=split, download=download)
+
+    if resize_inputs:
+        resize_kwargs = {"patch_shape": patch_shape, "is_rgb": False}
+        kwargs, patch_shape = util.update_kwargs_for_resize_trafo(
+            kwargs=kwargs, patch_shape=patch_shape, resize_inputs=resize_inputs, resize_kwargs=resize_kwargs
+        )
+
+    dataset = torch_em.default_segmentation_dataset(
+        raw_paths=image_paths,
+        raw_key="data",
+        label_paths=gt_paths,
+        label_key="data",
+        patch_shape=patch_shape,
+        **kwargs
+    )
+
+    return dataset
+
+
+def get_micro_usp_loader(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, ...],
+    batch_size: int,
+    split: str,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataloader for segmentation of prostate in micro-ultrasound scans. See `get_micro_usp_dataset` for details.
+    """
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_micro_usp_dataset(
+        path=path, patch_shape=patch_shape, split=split, resize_inputs=resize_inputs, download=download, **ds_kwargs
+    )
+    loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+    return loader