 b/evaluation_metrics.py
+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)