Source code for torchio.datasets.bite
import abc
from pathlib import Path
from typing import Dict
from typing import Optional
from .. import Image
from .. import LabelMap
from .. import ScalarImage
from .. import Subject
from .. import SubjectsDataset
from ..download import download_and_extract_archive
from ..transforms import Transform
from ..typing import TypePath
class BITE(SubjectsDataset, abc.ABC):
base_url = 'http://www.bic.mni.mcgill.ca/uploads/Services/'
def __init__(
self,
root: TypePath,
transform: Optional[Transform] = None,
download: bool = False,
**kwargs,
):
root = Path(root).expanduser().absolute()
if download:
self._download(root)
subjects_list = self._get_subjects_list(root)
self.kwargs = kwargs
super().__init__(subjects_list, transform=transform, **kwargs)
@abc.abstractmethod
def _download(self, root: Path):
raise NotImplementedError
@abc.abstractmethod
def _get_subjects_list(self, root: Path):
raise NotImplementedError
[docs]
class BITE3(BITE):
"""Pre- and post-resection MR images in BITE.
*The goal of BITE is to share in vivo medical images of patients wtith
brain tumors to facilitate the development and validation of new image
processing algorithms.*
Please check the `BITE website`_ for more information and
acknowledgments instructions.
.. _BITE website: http://nist.mni.mcgill.ca/bite-brain-images-of-tumors-for-evaluation-database/
Args:
root: Root directory to which the dataset will be downloaded.
transform: An instance of
:class:`~torchio.transforms.transform.Transform`.
download: If set to ``True``, will download the data into :attr:`root`.
""" # noqa: B950
dirname = 'group3'
def _download(self, root: Path):
if (root / self.dirname).is_dir():
return
root.mkdir(exist_ok=True, parents=True)
filename = f'{self.dirname}.tar.gz'
url = self.base_url + filename
download_and_extract_archive(
url,
download_root=root,
md5='e415b63887c40b727c45552614b44634',
)
(root / filename).unlink() # cleanup
def _get_subjects_list(self, root: Path):
subjects_dir = root / self.dirname
subjects = []
for i in range(1, 15):
if i == 13:
continue # no MRI for this subject
subject_id = f'{i:02d}'
subject_dir = subjects_dir / subject_id
preop_path = subject_dir / f'{subject_id}_preop_mri.mnc'
postop_path = subject_dir / f'{subject_id}_postop_mri.mnc'
images_dict: Dict[str, Image] = {}
images_dict['preop'] = ScalarImage(preop_path)
images_dict['postop'] = ScalarImage(postop_path)
for fp in subject_dir.glob('*tumor*'):
images_dict[fp.stem[3:]] = LabelMap(fp)
subject = Subject(images_dict)
subjects.append(subject)
return subjects