CerebNet.datasets.utils

class CerebNet.datasets.utils.LTADict

Methods

clear()
copy()
fromkeys(iterable[, value])	Create a new dictionary with keys from iterable and values set to value.
get(key[, default])	Return the value for key if key is in the dictionary, else default.
items()
keys()
pop(key[, default])	If the key is not found, return the default if given; otherwise, raise a KeyError.
popitem(/)	Remove and return a (key, value) pair as a 2-tuple.
setdefault(key[, default])	Insert key with a value of default if key is not in the dictionary.
update([E, ]**F)	If E is present and has a .keys() method, then does: for k in E: D[k] = E[k] If E is present and lacks a .keys() method, then does: for k, v in E: D[k] = v In either case, this is followed by: for k in F: D[k] = F[k]
values()

CerebNet.datasets.utils.bounding_volume_offset(img, target_img_size, image_shape=None)

Find the center of the non-zero values in img and returns offsets so this center is in the center of a bounding volume of size target_img_size.

CerebNet.datasets.utils.filter_blank_slices_thick(data_dict, threshold=10)

Function to filter blank slices from the volume using the label volume :param dict data_dict: dictionary containing all volumes need to be filtered :return:

CerebNet.datasets.utils.map_label2subseg(mapped_subseg, label_type='cereb_subseg')

Function to perform look-up table mapping from label space to subseg space

CerebNet.datasets.utils.map_size(arr, base_shape, return_border=False)

Resize the image to base_shape.

CerebNet.datasets.utils.read_lta(file)

Read the LTA info.