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import abc |
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from pathlib import Path |
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from typing import Optional |
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from ..typing import TypePath |
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from ..transforms import Transform |
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from ..download import download_and_extract_archive |
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from .. import SubjectsDataset, Subject, ScalarImage, LabelMap |
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class BITE(SubjectsDataset, abc.ABC): |
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base_url = 'http://www.bic.mni.mcgill.ca/uploads/Services/' |
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def __init__( |
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self, |
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root: TypePath, |
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transform: Optional[Transform] = None, |
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download: bool = False, |
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**kwargs, |
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): |
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root = Path(root).expanduser().absolute() |
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if download: |
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self._download(root) |
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subjects_list = self._get_subjects_list(root) |
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self.kwargs = kwargs |
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super().__init__(subjects_list, transform=transform, **kwargs) |
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def _download(self, root: Path): |
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raise NotImplementedError |
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def _get_subjects_list(self, root: Path): |
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raise NotImplementedError |
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class BITE3(BITE): |
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dirname = 'group3' |
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"""Pre- and post-resection MR images in BITE. |
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*The goal of BITE is to share in vivo medical images of patients wtith |
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brain tumors to facilitate the development and validation of new image |
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processing algorithms.* |
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Please check the `BITE website`_ for more information and |
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acknowledgments instructions. |
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.. _BITE website: http://nist.mni.mcgill.ca/?page_id=672 |
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Args: |
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root: Root directory to which the dataset will be downloaded. |
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transform: An instance of |
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:class:`~torchio.transforms.transform.Transform`. |
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download: If set to ``True``, will download the data into :attr:`root`. |
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""" |
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def _download(self, root: Path): |
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if (root / self.dirname).is_dir(): |
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return |
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root.mkdir(exist_ok=True, parents=True) |
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filename = f'{self.dirname}.tar.gz' |
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url = self.base_url + filename |
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download_and_extract_archive( |
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url, |
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download_root=root, |
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md5='e415b63887c40b727c45552614b44634', |
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) |
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(root / filename).unlink() # cleanup |
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def _get_subjects_list(self, root: Path): |
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subjects_dir = root / self.dirname |
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subjects = [] |
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for i in range(1, 15): |
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if i == 13: |
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continue # no MRI for this subject |
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subject_id = f'{i:02d}' |
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subject_dir = subjects_dir / subject_id |
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preop_path = subject_dir / f'{subject_id}_preop_mri.mnc' |
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postop_path = subject_dir / f'{subject_id}_postop_mri.mnc' |
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images_dict = {} |
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images_dict['preop'] = ScalarImage(preop_path) |
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images_dict['postop'] = ScalarImage(postop_path) |
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for fp in subject_dir.glob('*tumor*'): |
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images_dict[fp.stem[3:]] = LabelMap(fp) |
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subject = Subject(images_dict) |
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subjects.append(subject) |
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return subjects |
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fepegar /
torchio
