import abc
from pathlib import Path
from typing import Optional
from ..typing import TypePath
from ..transforms import Transform
from ..download import download_and_extract_archive
from .. import SubjectsDataset, Subject, ScalarImage, LabelMap
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)
def _download(self, root: Path):
raise NotImplementedError
def _get_subjects_list(self, root: Path):
raise NotImplementedError
[docs]class BITE3(BITE):
dirname = 'group3'
"""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/?page_id=672
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`.
"""
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 = {}
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