CorpusCallosum.utils.types¶
- class CorpusCallosum.utils.types.CCMeasuresDict[source]¶
TypedDict for corpus callosum measures.
Attributes
cc_index
(float) Corpus callosum shape index.
circularity
(float) Shape circularity measure.
areas
(np.ndarray) Areas of subdivided regions.
midline_length
(float) Length along the midline.
thickness
(float) Array of thickness measurements.
curvature
(float) Array of curvature measurements.
thickness_profile
(np.ndarray of type float) Thickness measurements along the contour.
total_area
(float) Total area of the CC.
total_perimeter
(float) Total perimeter length.
split_contours
(list of np.ndarray) Subdivided contour segments in AS-slice coordinates.
midline_equidistant
(np.ndarray) Equidistant points along midline in AS-slice coordinates.
levelpaths
(list of np.ndarray) Paths for thickness measurements in AS-slice coordinates.
slice_index
(int) Index of the processed slice.
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()
- CorpusCallosum.utils.types.ContourList[source]¶
alias of
list[type[ndarray[tuple[Literal[2],int],dtype[ScalarType]]]]