FastSurferCNN.data_loader.data_utils¶

FastSurferCNN.data_loader.data_utils.bbox_3d(img)[source]¶

Extract the three-dimensional bounding box coordinates.

Parameters:

imgnpt.NDArray: Mri image.

Returns:

np.ndarray: Rmin.
np.ndarray: Rmax.
np.ndarray: Cmin.
np.ndarray: Cmax.
np.ndarray: Zmin.
np.ndarray: Zmax.

FastSurferCNN.data_loader.data_utils.clean_cortex_labels(aparc)[source]¶

Clean up aparc segmentations.

Map undetermined and optic chiasma to BKG Map Hypointensity classes to one Vessel to WM 5th Ventricle to CSF Remaining cortical labels to BKG.

Parameters:

aparcnpt.NDArray: Aparc segmentations.

Returns:

np.ndarray: Cleaned aparc.

FastSurferCNN.data_loader.data_utils.cortex_border_mask(label, structure, ctx_thresh=33)[source]¶

Erode the cortex of a given mri image to create the inner gray matter mask (outer most cortex voxels).

Parameters:

labelnpt.NDArray: Ground truth labels.
structurenpt.NDArray: Structuring element to erode with.
ctx_threshint: Label value of cortex (above = cortical parcels). Defaults to 33.

Returns:

np.ndarray: Inner grey matter layer.

FastSurferCNN.data_loader.data_utils.create_weight_mask(mapped_aseg, max_weight=5, max_edge_weight=5, max_hires_weight=None, ctx_thresh=33, mean_filter=False, cortex_mask=True, gradient=True)[source]¶

Create weighted mask - with median frequency balancing and edge-weighting.

Parameters:

mapped_asegnp.ndarray: Segmentation to create weight mask from.
max_weightint: Maximal weight on median weights (cap at this value). (Default value = 5).
max_edge_weightint: Maximal weight on gradient weight (cap at this value). (Default value = 5).
max_hires_weightint: Maximal weight on hires weight (cap at this value). (Default value = None).
ctx_threshint: Label value of cortex (above = cortical parcels). (Default value = 33).
mean_filterbool: Flag, set to add mean_filter mask (default = False).
cortex_maskbool: Flag, set to create cortex weight mask (default=True).
gradientbool: Flag, set to create gradient mask (default = True).

Returns:

np.ndarray: Weights.

FastSurferCNN.data_loader.data_utils.deep_sulci_and_wm_strand_mask(volume, structure, iteration=1, ctx_thresh=33)[source]¶

Get a binary mask of deep sulci and small white matter strands by using binary closing (erosion and dilation).

Parameters:

volumenpt.NDArray: Loaded image (aseg, label space).
structurenpt.NDArray: Structuring element (e.g. np.ones((3, 3, 3))).
iterationint: Number of times mask should be dilated + eroded. Defaults to 1.
ctx_threshint: Label value of cortex (above = cortical parcels). Defaults to 33.

Returns:

np.ndarray: Sulcus + wm mask.

FastSurferCNN.data_loader.data_utils.fill_unknown_labels_per_hemi(gt, unknown_label, cortex_stop)[source]¶

Replace label 1000 (lh unknown) and 2000 (rh unknown) with closest class for each voxel.

Parameters:

gtnpt.NDArray: Ground truth segmentation with class unknown.
unknown_labelint: Class label for unknown (lh: 1000, rh: 2000).
cortex_stopint: Class label at which cortical labels of this hemi stop (lh: 2000, rh: 3000).

Returns:

np.ndarray: Ground truth segmentation with all classes.

FastSurferCNN.data_loader.data_utils.filter_blank_slices_thick(img_vol, label_vol, weight_vol, threshold=50)[source]¶

Filter blank slices from the volume using the label volume.

Parameters:

img_volnpt.NDArray: Orig image volume.
label_volnpt.NDArray: Label images (ground truth).
weight_volnpt.NDArray: Weight corresponding to labels.
thresholdint: Threshold for number of pixels needed to keep slice (below = dropped). (Default value = 50).

Returns:

filtered img_volnp.ndarray: Image volume with blank slices removed.
label_volnp.ndarray: Label volume with blank slices removed.
weight_volnp.ndarray: Weight volume with blank slices removed.

FastSurferCNN.data_loader.data_utils.fuse_cortex_labels(aparc)[source]¶

Fuse cortical parcels on left/right hemisphere (reduce aparc classes).

Parameters:

aparcnpt.NDArray: Anatomical segmentation with cortical parcels.

Returns:

np.ndarray: Anatomical segmentation with reduced number of cortical parcels.

FastSurferCNN.data_loader.data_utils.get_labels_from_lut(lut, label_extract=('Left-', 'ctx-rh'))[source]¶

Extract labels from the lookup tables.

Parameters:

lutUnion[str, pd.DataFrame]: FreeSurfer like LookUp Table (either path to it or already loaded as pandas DataFrame. Example entry: ID LabelName R G B A 0 Unknown 0 0 0 0 1 Left-Cerebral-Exterior 70 130 180 0.
label_extractTuple[str, str]: Suffix of label names to mask for sagittal labels Default: “Left-” and “ctx-rh”.

Returns:

np.ndarray: Full label list.
np.ndarray: Sagittal label list.

FastSurferCNN.data_loader.data_utils.get_largest_cc(segmentation)[source]¶

Find the largest connected component of segmentation.

Parameters:

segmentationnpt.NDArray: Segmentation.

Returns:

np.ndarray: Largest connected component of segmentation (binary mask).

FastSurferCNN.data_loader.data_utils.get_thick_slices(img_data, slice_thickness=3)[source]¶

Extract thick slices from the image.

Feed slice_thickness preceding and succeeding slices to network, label only middle one.

Parameters:

img_datanpt.NDArray: 3D MRI image read in with nibabel.
slice_thicknessint: Number of slices to stack on top and below slice of interest (default=3).

Returns:

np.ndarray: Image data with the thick slices of the n-th axis appended into the n+1-th axis.

FastSurferCNN.data_loader.data_utils.infer_mapping_from_lut(num_classes_full, lut)[source]¶

Guess the mapping from a lookup table.

Parameters:

num_classes_fullint: Number of classes.
lutUnion[str, pd.DataFrame]: Look-up table listing class labels.

Returns:

np.ndarray: List of indexes for.

FastSurferCNN.data_loader.data_utils.load_and_conform_image(img_filename, order=1, logger=<Logger FastSurferCNN.data_loader.data_utils (INFO)>, **conform_kwargs)[source]¶

Load MRI image and conform it to UCHAR, RAS orientation and 1mm or minimum isotropic voxels size.

Only, if it does not already have this format.

Parameters:

img_filenamePath, str: Path and name of volume to read.
orderint, default=1: Interpolation order for image conformation (0=nearest, 1=linear(default), 2=quadratic, 3=cubic).
loggerlogging.Logger, default=<local logger>: Logger to write output to (default = STDOUT).
**conform_kwargs: Additional parameters to conform and is_conform.

Returns:

nibabel.Header header_info: Header information of the conformed image.
numpy.ndarray affine_info: Affine information of the conformed image.
numpy.ndarray orig_data: Conformed image data.

Raises:

RuntimeError: If input has multiple input frames or inconsistent nifti headers.

FastSurferCNN.data_loader.data_utils.load_image(file, name='image', **kwargs)[source]¶

Load file ‘file’ with nibabel, including all data.

Parameters:

filePath, str: Path to the file to load.
namestr, default=”image”: Name of the file (optional), only effects error messages.
**kwargs: Additional keyword arguments.

Returns:

Tuple[nib.analyze.SpatialImage, np.ndarray]: The nibabel image object and a numpy array of the data.

Raises:

IOError: Failed loading the file nibabel releases the GIL, so the following is a parallel example. { >>> from concurrent.futures import ThreadPoolExecutor >>> with ThreadPoolExecutor() as pool: >>> future1 = pool.submit(load_image, filename1) >>> future2 = pool.submit(load_image, filename2) >>> image, data = future1.result() >>> image2, data2 = future2.result() }

FastSurferCNN.data_loader.data_utils.load_maybe_conform(file, alt_file, **conform_kwargs)[source]¶

Load an image by file, check whether it is conformed to vox_size and conform to vox_size if it is not.

Parameters:

filePath, str: Path to the file to load.
alt_filePath, str: Alternative file to interpolate from.
**conform_kwargs: Additional parameters to conform and is_conform.

Returns:

Path: The path to the file.
nib.analyze.SpatialImage: The file container object including the corrected header.
np.ndarray: The data loaded from the file.