Add BUSI dataset (#268)

Add BUSI dataset

scripts/datasets/check_busi.py

@@ -0,0 +1,22 @@
+from torch_em.util.debug import check_loader
+from torch_em.data.datasets.medical import get_busi_loader
+
+
+ROOT = "/media/anwai/ANWAI/data/busi"
+
+
+def check_busi():
+    loader = get_busi_loader(
+        path=ROOT,
+        patch_shape=(512, 512),
+        batch_size=2,
+        category=None,
+        resize_inputs=False,
+        download=True,
+    )
+
+    check_loader(loader, 8)
+
+
+if __name__ == "__main__":
+    check_busi()

torch_em/data/datasets/medical/__init__.py

@@ -1,5 +1,6 @@
 from .autopet import get_autopet_loader
 from .btcv import get_btcv_dataset, get_btcv_loader
+from .busi import get_busi_dataset, get_busi_loader
 from .camus import get_camus_dataset, get_camus_loader
 from .drive import get_drive_dataset, get_drive_loader
 from .papila import get_papila_dataset, get_papila_loader

torch_em/data/datasets/medical/busi.py

@@ -0,0 +1,105 @@
+import os
+from glob import glob
+from typing import Union, Tuple, Optional
+
+import torch_em
+from torch_em.transform.generic import ResizeInputs
+
+from .. import util
+from ... import ImageCollectionDataset
+
+
+URL = "https://scholar.cu.edu.eg/Dataset_BUSI.zip"
+CHECKSUM = "b2ce09f6063a31a73f628b6a6ee1245187cbaec225e93e563735691d68654de7"
+
+
+def get_busi_data(path, download):
+    os.makedirs(path, exist_ok=True)
+
+    data_dir = os.path.join(path, "Dataset_BUSI_with_GT")
+    if os.path.exists(data_dir):
+        return data_dir
+
+    zip_path = os.path.join(path, "Dataset_BUSI.zip")
+    util.download_source(path=zip_path, url=URL, download=download, checksum=CHECKSUM, verify=False)
+    util.unzip(zip_path=zip_path, dst=path)
+
+    return data_dir
+
+
+def _get_busi_paths(path, category, download):
+    data_dir = get_busi_data(path=path, download=download)
+
+    if category is None:
+        category = "*"
+
+    data_dir = os.path.join(data_dir, category)
+
+    image_paths = sorted(glob(os.path.join(data_dir, r"*).png")))
+    gt_paths = sorted(glob(os.path.join(data_dir, r"*)_mask.png")))
+
+    return image_paths, gt_paths
+
+
+def get_busi_dataset(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    category: Optional[str] = None,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """"Dataset for segmentation of breast cancer in ultrasound images.
+
+    This database is located at https://scholar.cu.edu.eg/?q=afahmy/pages/dataset
+
+    The dataset is from Al-Dhabyani et al. - https://doi.org/10.1016/j.dib.2019.104863
+    Please cite it if you use this dataset for a publication.
+    """
+    if category is not None:
+        assert category in ["normal", "benign", "malignant"]
+
+    image_paths, gt_paths = _get_busi_paths(path=path, category=category, download=download)
+
+    if resize_inputs:
+        raw_trafo = ResizeInputs(target_shape=patch_shape, is_rgb=True)
+        label_trafo = ResizeInputs(target_shape=patch_shape, is_label=True)
+        patch_shape = None
+    else:
+        patch_shape = patch_shape
+        raw_trafo, label_trafo = None, None
+
+    dataset = ImageCollectionDataset(
+        raw_image_paths=image_paths,
+        label_image_paths=gt_paths,
+        patch_shape=patch_shape,
+        raw_transform=raw_trafo,
+        label_transform=label_trafo,
+        **kwargs
+    )
+
+    return dataset
+
+
+def get_busi_loader(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    batch_size: int,
+    category: Optional[str] = None,
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataloader for segmentation of breast cancer in ultrasound images. See `get_busi_dataset` for details.
+    """
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_busi_dataset(
+        path=path,
+        patch_shape=patch_shape,
+        category=category,
+        resize_inputs=resize_inputs,
+        download=download,
+        **ds_kwargs
+    )
+    loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+    return loader