import numpy as np
from scipy import ndimage
def binary_dice3d(s,g):
#dice score of two 3D volumes
num=np.sum(np.multiply(s, g))
denom=s.sum() + g.sum()
if denom==0:
return 1
else:
return 2.0*num/denom
def sensitivity (seg,ground):
#computs false negative rate
num=np.sum(np.multiply(ground, seg ))
denom=np.sum(ground)
if denom==0:
return 1
else:
return num/denom
def specificity (seg,ground):
#computes false positive rate
num=np.sum(np.multiply(ground==0, seg ==0))
denom=np.sum(ground==0)
if denom==0:
return 1
else:
return num/denom
def border_map(binary_img,neigh):
"""
Creates the border for a 3D image
"""
binary_map = np.asarray(binary_img, dtype=np.uint8)
neigh = neigh
west = ndimage.shift(binary_map, [-1, 0,0], order=0)
east = ndimage.shift(binary_map, [1, 0,0], order=0)
north = ndimage.shift(binary_map, [0, 1,0], order=0)
south = ndimage.shift(binary_map, [0, -1,0], order=0)
top = ndimage.shift(binary_map, [0, 0, 1], order=0)
bottom = ndimage.shift(binary_map, [0, 0, -1], order=0)
cumulative = west + east + north + south + top + bottom
border = ((cumulative < 6) * binary_map) == 1
return border
def border_distance(ref,seg):
"""
This functions determines the map of distance from the borders of the
segmentation and the reference and the border maps themselves
"""
neigh=8
border_ref = border_map(ref,neigh)
border_seg = border_map(seg,neigh)
oppose_ref = 1 - ref
oppose_seg = 1 - seg
# euclidean distance transform
distance_ref = ndimage.distance_transform_edt(oppose_ref)
distance_seg = ndimage.distance_transform_edt(oppose_seg)
distance_border_seg = border_ref * distance_seg
distance_border_ref = border_seg * distance_ref
return distance_border_ref, distance_border_seg#, border_ref, border_seg
def Hausdorff_distance(ref,seg):
"""
This functions calculates the average symmetric distance and the
hausdorff distance between a segmentation and a reference image
:return: hausdorff distance and average symmetric distance
"""
ref_border_dist, seg_border_dist = border_distance(ref,seg)
hausdorff_distance = np.max(
[np.max(ref_border_dist), np.max(seg_border_dist)])
return hausdorff_distance
def DSC_whole(pred, orig_label):
#computes dice for the whole tumor
return binary_dice3d(pred>0,orig_label>0)
def DSC_en(pred, orig_label):
#computes dice for enhancing region
return binary_dice3d(pred==4,orig_label==4)
def DSC_core(pred, orig_label):
#computes dice for core region
seg_=np.copy(pred)
ground_=np.copy(orig_label)
seg_[seg_==2]=0
ground_[ground_==2]=0
return binary_dice3d(seg_>0,ground_>0)
def sensitivity_whole (seg,ground):
return sensitivity(seg>0,ground>0)
def sensitivity_en (seg,ground):
return sensitivity(seg==4,ground==4)
def sensitivity_core (seg,ground):
seg_=np.copy(seg)
ground_=np.copy(ground)
seg_[seg_==2]=0
ground_[ground_==2]=0
return sensitivity(seg_>0,ground_>0)
def specificity_whole (seg,ground):
return specificity(seg>0,ground>0)
def specificity_en (seg,ground):
return specificity(seg==4,ground==4)
def specificity_core (seg,ground):
seg_=np.copy(seg)
ground_=np.copy(ground)
seg_[seg_==2]=0
ground_[ground_==2]=0
return specificity(seg_>0,ground_>0)
def hausdorff_whole (seg,ground):
return Hausdorff_distance(seg==0,ground==0)
def hausdorff_en (seg,ground):
return Hausdorff_distance(seg!=4,ground!=4)
def hausdorff_core (seg,ground):
seg_=np.copy(seg)
ground_=np.copy(ground)
seg_[seg_==2]=0
ground_[ground_==2]=0
return Hausdorff_distance(seg_==0,ground_==0)
