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import csv |
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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 EPISURG(SubjectsDataset): |
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""" |
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`EPISURG`_ is a clinical dataset of :math:`T_1`-weighted MRI from 430 |
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epileptic patients who underwent resective brain surgery at the National |
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Hospital of Neurology and Neurosurgery (Queen Square, London, United |
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Kingdom) between 1990 and 2018. |
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The dataset comprises 430 postoperative MRI. The corresponding preoperative |
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MRI is present for 268 subjects. |
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Three human raters segmented the resection cavity on partially overlapping |
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subsets of EPISURG. |
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If you use this dataset for your research, you agree with the *Data use |
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agreement* presented at the EPISURG entry on the `UCL Research Data |
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Repository <EPISURG>`_ and you must cite the corresponding publications. |
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.. _EPISURG: https://doi.org/10.5522/04/9996158.v1 |
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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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.. warning:: The size of this dataset is multiple GB. |
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If you set :attr:`download` to ``True``, it will take some time |
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to be downloaded if it is not already present. |
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""" |
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data_url = 'https://s3-eu-west-1.amazonaws.com/pstorage-ucl-2748466690/26153588/EPISURG.zip' # noqa: E501 |
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md5 = '5ec5831a2c6fbfdc8489ba2910a6504b' |
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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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@staticmethod |
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def _check_exists(root, modalities): |
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for modality in modalities: |
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modality_dir = root / modality |
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if not modality_dir.is_dir(): |
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exists = False |
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break |
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else: |
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exists = True |
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return exists |
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@staticmethod |
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def _get_subjects_list(root): |
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episurg_dir = root / 'EPISURG' |
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subjects_dir = episurg_dir / 'subjects' |
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csv_path = episurg_dir / 'subjects.csv' |
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with open(csv_path) as csvfile: |
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reader = csv.DictReader(csvfile) |
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subjects = [] |
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for row in reader: |
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subject_id = row['Subject'] |
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subject_dir = subjects_dir / subject_id |
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subject_dict = { |
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'subject_id': subject_id, |
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'hemisphere': row['Hemisphere'], |
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'surgery_type': row['Type'], |
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} |
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preop_dir = subject_dir / 'preop' |
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preop_paths = list(preop_dir.glob('*preop*')) |
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assert len(preop_paths) <= 1 |
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if preop_paths: |
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subject_dict['preop_mri'] = ScalarImage(preop_paths[0]) |
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postop_dir = subject_dir / 'postop' |
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postop_path = list(postop_dir.glob('*postop-t1mri*'))[0] |
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subject_dict['postop_mri'] = ScalarImage(postop_path) |
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for seg_path in postop_dir.glob('*seg*'): |
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seg_id = seg_path.name[-8] |
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subject_dict[f'seg_{seg_id}'] = LabelMap(seg_path) |
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subjects.append(Subject(**subject_dict)) |
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return subjects |
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def _download(self, root): |
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"""Download the EPISURG data if it does not exist already.""" |
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if (root / 'EPISURG').is_dir(): |
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return |
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root.mkdir(exist_ok=True, parents=True) |
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download_and_extract_archive( |
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self.data_url, |
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download_root=root, |
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md5=self.md5, |
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) |
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(root / 'EPISURG.zip').unlink() # cleanup |
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def _glob_subjects(self, string): |
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subjects = [] |
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for subject in self._subjects: |
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for image_name in subject: |
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if string in image_name: |
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subjects.append(subject) |
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break |
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return subjects |
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def _get_labeled_subjects(self): |
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return self._glob_subjects('seg') |
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def _get_paired_subjects(self): |
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return self._glob_subjects('preop') |
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def _get_subset(self, subjects): |
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dataset = SubjectsDataset( |
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subjects, |
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transform=self._transform, |
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**(self.kwargs), |
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) |
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return dataset |
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def get_labeled(self) -> SubjectsDataset: |
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"""Get dataset from subjects with manual annotations.""" |
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return self._get_subset(self._get_labeled_subjects()) |
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def get_unlabeled(self) -> SubjectsDataset: |
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"""Get dataset from subjects without manual annotations.""" |
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subjects = [ |
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s for s in self._subjects |
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if s not in self._get_labeled_subjects() |
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] |
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return self._get_subset(subjects) |
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def get_paired(self) -> SubjectsDataset: |
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"""Get dataset from subjects with pre- and post-op MRI.""" |
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return self._get_subset(self._get_paired_subjects()) |
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fepegar /
torchio
