"""
Contains various functions for computing statistics over 3D volumes
"""
import numpy as np
def Dice3d(a, b):
"""
This will compute the Dice Similarity coefficient for two 3-dimensional volumes
Volumes are expected to be of the same size. We are expecting binary masks -
0's are treated as background and anything else is counted as data
Arguments:
a {Numpy array} -- 3D array with first volume
b {Numpy array} -- 3D array with second volume
Returns:
float
"""
if len(a.shape) != 3 or len(b.shape) != 3:
raise Exception(f"Expecting 3 dimensional inputs, got {a.shape} and {b.shape}")
if a.shape != b.shape:
raise Exception(f"Expecting inputs of the same shape, got {a.shape} and {b.shape}")
# TASK: Write implementation of Dice3D. If you completed exercises in the lessons
# you should already have it.
a = a > 0
b = b > 0
a[a>0] = 1
b[b>0] = 1
#a, b = (a > 0).astype(int), (b > 0).astype(int)
intersection = np.sum(a*b)
volumes = np.sum(a) + np.sum(b)
if volumes == 0:
return -1
else:
return 2.*float(intersection) / float(volumes)
def Jaccard3d(a, b):
"""
This will compute the Jaccard Similarity coefficient for two 3-dimensional volumes
Volumes are expected to be of the same size. We are expecting binary masks -
0's are treated as background and anything else is counted as data
Arguments:
a {Numpy array} -- 3D array with first volume
b {Numpy array} -- 3D array with second volume
Returns:
float
"""
if len(a.shape) != 3 or len(b.shape) != 3:
raise Exception(f"Expecting 3 dimensional inputs, got {a.shape} and {b.shape}")
if a.shape != b.shape:
raise Exception(f"Expecting inputs of the same shape, got {a.shape} and {b.shape}")
# TASK: Write implementation of Jaccard similarity coefficient. Please do not use
# the Dice3D function from above to do the computation ;)
a = a > 0
b = b > 0
a[a>0] = 1
b[b>0] = 1
#a, b = (a > 0).astype(int), (b > 0).astype(int)
intersection = np.sum(a*b)
volumes = np.sum(a) + np.sum(b)
if volumes == 0:
return -1
else:
return float(intersection)/float((volumes - intersection))
