Dev

amid/__init__.py

@@ -19,6 +19,7 @@
 from .lidc import LIDC
 from .lits import LiTS
 from .liver_medseg import LiverMedseg
+from .luna25 import LUNA25
 from .medseg9 import Medseg9
 from .midrc import MIDRC
 from .mood import MOOD

amid/__version__.py

@@ -1 +1 @@
-__version__ = '0.14.0'
+__version__ = '0.14.1'

amid/luna25.py

@@ -0,0 +1,138 @@
+from datetime import datetime
+from functools import cached_property
+from typing import Dict, NamedTuple, Sequence
+
+import numpy as np
+import pandas as pd
+import SimpleITK as sitk
+
+from .internals import Dataset, field, licenses, register
+
+
+class NoduleBlock(NamedTuple):
+    image: np.ndarray
+    metadata: Dict
+
+
+class LUNA25Nodule(NamedTuple):
+    coords: Sequence[float]
+    lesion_id: int
+    annotation_id: str
+    nodule_id: str
+    malignancy: bool
+    center_voxel: Sequence[float]
+    bbox: np.ndarray
+
+
+@register(
+    body_region='Chest',
+    license=licenses.CC_BY_40,
+    link='https://luna25.grand-challenge.org/',
+    modality='CT',
+    prep_data_size=None,
+    raw_data_size=None,
+    task='Lung nodule malignancy risk estimation',
+)
+class LUNA25(Dataset):
+    """
+    The LUNA25 Challenge dataset is a comprehensive collection designed to support
+    the development and validation of AI algorithms for lung nodule malignancy risk
+    estimation using low-dose chest CT scans. In total, it contains 2120 patients
+    and 4069 low-dose chest CT scans, with 555 annotated malignant nodules and
+    5608 benign nodules (3762 unique nodules, 348 of them are malignant).
+    The dataset was acquired in participants who enrolled in the
+    National Lung Cancer Screening Trial (NLST) between 2002 and 2004 in
+    one of the 33 centers in the United States.
+
+    Parameters
+    ----------
+    root : str, Path, optional
+        path to the folder containing the raw downloaded archives.
+        If not provided, the cache is assumed to be already populated.
+
+    """
+
+    @property
+    def ids(self):
+        return [file.name[: -len('.mha')] for file in (self.root / 'luna25_images').iterdir()]
+
+    def _image(self, i):
+        return sitk.ReadImage(self.root / f'luna25_images/{i}.mha')
+
+    @field
+    def image(self, i):
+        return sitk.GetArrayFromImage(self._image(i))
+
+    @field
+    def spacing(self, i):
+        return self._image(i).GetSpacing()[::-1]
+
+    def _image_origin(self, i):
+        return self._image(i).GetOrigin()[::-1]
+
+    def _direction(self, i):
+        return self._image(i).GetDirection()[::-1]
+
+    @cached_property
+    def _data(self):
+        return pd.read_csv(self.root / 'LUNA25_Public_Training_Development_Data.csv')
+
+    def _data_rows(self, i):
+        return self._data[self._data['SeriesInstanceUID'] == i]
+
+    def _data_column_value(self, i, column_name):
+        values = self._data_rows(i).get(column_name).unique()
+        assert len(values) == 1
+        value = values[0]
+        assert not pd.isnull(value)
+        return value
+
+    @field
+    def patient_id(self, i):
+        return str(self._data_column_value(i, 'PatientID'))
+
+    @field
+    def study_date(self, i):
+        study_date = str(self._data_column_value(i, 'StudyDate'))
+        return datetime.strptime(study_date, "%Y%m%d").date()
+
+    @field
+    def age(self, i):
+        return self._data_column_value(i, 'Age_at_StudyDate')
+
+    @field
+    def sex(self, i):
+        return self._data_column_value(i, 'Gender')
+
+    @field
+    def nodules(self, i):
+        nodules = []
+        for row in self._data_rows(i).itertuples():
+            coords = np.array([row.CoordX, row.CoordY, row.CoordZ])
+            nodule_block_metadata = self.nodule_block_metadata(row.AnnotationID)
+            assert np.all(nodule_block_metadata['spacing'] == self.spacing(i))
+            image_origin = self._image_origin(i)
+            direction = np.array(self._direction(i)[::4])
+            center_voxel = ((coords[::-1] - image_origin) / self.spacing(i)) * direction
+            bbox_start_point = ((nodule_block_metadata['origin'] - image_origin) / self.spacing(i)) * direction
+            bbox = [bbox_start_point, np.minimum(bbox_start_point + np.array([64, 128, 128]), self.image(i).shape)]
+            nodules.append(
+                LUNA25Nodule(
+                    coords=coords,
+                    lesion_id=row.LesionID,
+                    annotation_id=str(row.AnnotationID),
+                    nodule_id=str(row.NoduleID),
+                    malignancy=row.label,
+                    center_voxel=np.round(center_voxel).astype(int),
+                    bbox=np.round(bbox).astype(int),
+                )
+            )
+        return nodules
+
+    def nodule_block_image(self, annotation_id):
+        return np.load(self.root / f'luna25_nodule_blocks/image/{annotation_id}.npy')
+
+    def nodule_block_metadata(self, annotation_id):
+        metadata = np.load(self.root / f'luna25_nodule_blocks/metadata/{annotation_id}.npy', allow_pickle=True)
+        assert metadata.shape == ()
+        return metadata.item()

docs/requirements.txt

@@ -1,10 +1,13 @@
-mkdocs
-mkdocs-material
-mkdocstrings[python]
-mkdocs-jupyter
-mkdocs-exclude
-mkdocs-autorefs
+mkdocs==1.5.3
+mkdocs-material==9.4.1
+mkdocstrings[python]==0.22.0
+mkdocs-jupyter==0.24.2
+mkdocs-exclude==1.0.2
+mkdocs-autorefs==0.4.1
 mike
 pandas
 tabulate
 ipython-genutils
+griffe==0.29.1
+mkdocs-material-extensions==1.2
+mkdocstrings-python==1.1.2