Add ARCADE dataset

scripts/datasets/medical/check_arcade.py

@@ -0,0 +1,24 @@
+from torch_em.util.debug import check_loader
+from torch_em.data import MinInstanceSampler
+from torch_em.data.datasets.medical import get_arcade_loader
+
+
+ROOT = "/media/anwai/ANWAI/data/arcade"
+
+
+def check_arcade():
+    loader = get_arcade_loader(
+        path=ROOT,
+        split="test",
+        patch_shape=(256, 256),
+        batch_size=2,
+        download=True,
+        task="syntax",
+        resize_inputs=False,
+        sampler=MinInstanceSampler(),
+    )
+    check_loader(loader, 8)
+
+
+if __name__ == "__main__":
+    check_arcade()

torch_em/data/datasets/medical/__init__.py

@@ -1,4 +1,5 @@
-from .autopet import get_autopet_loader
+from .arcade import get_arcade_dataset, get_arcade_loader
+from .autopet import get_autopet_dataset, 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

torch_em/data/datasets/medical/arcade.py

@@ -0,0 +1,190 @@
+import os
+from glob import glob
+from tqdm import tqdm
+from typing import Union, Tuple
+from collections import defaultdict
+
+import cv2
+import json
+import numpy as np
+import imageio.v3 as imageio
+
+import torch_em
+
+from .. import util
+
+
+URL = "https://zenodo.org/records/10390295/files/arcade.zip"
+CHECKSUM = "a396cdea7c92c55dc97bbf3dd8e3df517d76872b289a8bcb45513bdb3350837f"
+
+
+def get_arcade_data(path, download):
+    os.makedirs(path, exist_ok=True)
+
+    data_dir = os.path.join(path, "arcade")
+    if os.path.exists(data_dir):
+        return data_dir
+
+    zip_path = os.path.join(path, "arcade.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 _load_annotation_json(json_file):
+    assert os.path.exists(json_file)
+
+    with open(json_file, encoding="utf-8") as f:
+        gt_ann_json_file = json.load(f)
+
+    return gt_ann_json_file
+
+
+def _get_arcade_paths(path, split, task, download):
+    data_dir = get_arcade_data(path=path, download=download)
+
+    assert split in ["train", "val", "test"]
+
+    if task is None:
+        task = "*"
+
+    image_dirs = sorted(glob(os.path.join(data_dir, task, split, "images")))
+    gt_dirs = sorted(glob(os.path.join(data_dir, task, split, "annotations")))
+
+    image_paths, gt_paths = [], []
+    for image_dir, gt_dir in zip(image_dirs, gt_dirs):
+        json_file = os.path.join(gt_dir, f"{split}.json")
+        gt = _load_annotation_json(json_file)
+
+        # THE RECOMMENDED WAY FROM THE DATA PROVIDERS TO CONVERT FROM COCO TO MASKS #
+
+        gt_anns = defaultdict(list)
+
+        for ann in gt["annotations"]:
+            gt_anns[ann["image_id"]].append(ann)
+
+        for id, im in tqdm(gt_anns.items(), desc="Creating ARCADE segmentations from coco-style annotations"):
+            image_path = os.path.join(image_dir, f"{id}.png")
+            gt_path = os.path.join(gt_dir, f"{id}.tif")
+
+            image_paths.append(image_path)
+            gt_paths.append(gt_path)
+
+            if os.path.exists(gt_path):
+                continue
+
+            semantic_labels = np.zeros((512, 512), np.int32)
+            for ann in im:
+                points = np.array([ann["segmentation"][0][::2], ann["segmentation"][0][1::2]], np.int32).T
+                points = points.reshape(([-1, 1, 2]))
+                tmp = np.zeros((512, 512), np.int32)
+                cv2.fillPoly(semantic_labels, [points], (1))
+                semantic_labels += tmp
+
+            imageio.imwrite(gt_path, semantic_labels)
+
+        # DESIRED WAY #
+        # issues: the method does work quite nicely, however it does not work for some image ids (e.g. 1, 2, 922)
+        # while works for others (923, 924)
+
+        # from pycocotools.coco import COCO
+        # import numpy as np
+        # import imageio.v3 as imageio
+
+        # coco = COCO(json_file)
+
+        # image_ids = coco.getImgIds()
+        # image_id = 925  # image_ids[0]
+        # image_metadata = coco.loadImgs(image_id)[0]
+        # fname = image_metadata["file_name"]
+
+        # ann_ids = coco.getAnnIds(imgIds=image_metadata["id"])
+        # anns = coco.loadAnns(ann_ids)
+
+        # shape = (image_metadata["height"], image_metadata["width"])
+        # seg = np.zeros(shape, dtype="uint32")
+
+        # for ann in anns:
+        #     mask = coco.annToMask(ann).astype("bool")
+        #     seg[mask] = 1
+
+        # image = imageio.imread(os.path.join(image_dir, fname))
+
+        # import napari
+
+        # v = napari.Viewer()
+        # v.add_image(image)
+        # v.add_image(seg > 0)
+        # napari.run()
+
+        # breakpoint()
+
+    return image_paths, gt_paths
+
+
+def get_arcade_dataset(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    split: str,
+    task: str = "syntax",
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataset for coronary vessel segmentation in x-ray coronary angiography.
+
+    The database is located at https://doi.org/10.5281/zenodo.10390295.
+
+    This dataset is from the "ARCADE" challenge - https://doi.org/10.1038/s41597-023-02871-z.
+    Please cite it if you use this dataset for a publication.
+    """
+    # TODO: the "stenosis" data has 3 channels, the "syntax" data has 1 channel
+    # for us, the relevant one is the "syntax" task, as we are interest in segmenting vessels for our workflows.
+    # for the "stenosis" task, the segmentations are only for the
+    # "stenotic valves" (i.e. abnormal narrowing of a certain region of the arteries)
+    image_paths, gt_paths = _get_arcade_paths(path=path, split=split, task=task, 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=None,
+        label_paths=gt_paths,
+        label_key=None,
+        patch_shape=patch_shape,
+        is_seg_dataset=False,
+        **kwargs
+    )
+
+    return dataset
+
+
+def get_arcade_loader(
+    path: Union[os.PathLike, str],
+    patch_shape: Tuple[int, int],
+    batch_size: int,
+    split: str,
+    task: str = "syntax",
+    resize_inputs: bool = False,
+    download: bool = False,
+    **kwargs
+):
+    """Dataloader for coronary vessel segmentation in x-ray coronary angiography. See `get_arcade_dataset` for details.
+    """
+    ds_kwargs, loader_kwargs = util.split_kwargs(torch_em.default_segmentation_dataset, **kwargs)
+    dataset = get_arcade_dataset(
+        path=path,
+        patch_shape=patch_shape,
+        split=split,
+        task=task,
+        resize_inputs=resize_inputs,
+        download=download,
+        **ds_kwargs
+    )
+    loader = torch_em.get_data_loader(dataset=dataset, batch_size=batch_size, **loader_kwargs)
+    return loader