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evaluation.py
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evaluation.py
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import torch
from medpy.metric import binary
# SR : Segmentation Result
# GT : Ground Truth
def get_accuracy(SR,GT,threshold=0.5):
SR = SR > threshold
GT = GT == torch.max(GT)
corr = torch.sum(SR==GT)
tensor_size = SR.size(0)*SR.size(1)*SR.size(2)*SR.size(3)
acc = float(corr)/float(tensor_size)
return acc
def get_sensitivity(SR,GT,threshold=0.5):
# Sensitivity == Recall
SR = SR > threshold
GT = GT == torch.max(GT)
# TP : True Positive
# FN : False Negative
# TP = ((SR==1)+(GT==1))==2
# FN = ((SR==0)+(GT==1))==2
#
# SE = float(torch.sum(TP))/(float(torch.sum(TP+FN)) + 1e-6)
SE = binary.sensitivity(SR.cpu().numpy(),GT.cpu().numpy())
return SE
def get_specificity(SR,GT,threshold=0.5):
SR = SR > threshold
GT = GT == torch.max(GT)
# TN : True Negative
# FP : False Positive
# TN = ((SR==0)+(GT==0))==2
# FP = ((SR==1)+(GT==0))==2
#
# SP = float(torch.sum(TN))/(float(torch.sum(TN+FP)) + 1e-6)
SP = binary.specificity(SR.cpu().numpy(),GT.cpu().numpy())
return SP
def get_precision(SR,GT,threshold=0.5):
SR = SR > threshold
GT = GT == torch.max(GT)
# TP : True Positive
# FP : False Positive
# TP = ((SR==1)+(GT==1))==2
# FP = ((SR==1)+(GT==0))==2
#
# PC = float(torch.sum(TP))/(float(torch.sum(TP+FP)) + 1e-6)
PC = binary.precision(SR.cpu().numpy(),GT.cpu().numpy())
return PC
def get_F1(SR,GT,threshold=0.5):
# Sensitivity == Recall
SE = get_sensitivity(SR,GT,threshold=threshold)
PC = get_precision(SR,GT,threshold=threshold)
F1 = 2*SE*PC/(SE+PC + 1e-6)
return F1
def get_JS(SR,GT,threshold=0.5):
# JS : Jaccard similarity
SR = SR > threshold
GT = GT == torch.max(GT)
# Inter = torch.sum((SR+GT)==2)
# Union = torch.sum((SR+GT)>=1)
#
# JS = float(Inter)/(float(Union) + 1e-6)
JS = binary.jc(SR.cpu().numpy(),GT.cpu().numpy())
return JS
def get_DC(SR,GT,threshold=0.5):
# DC : Dice Coefficient
SR = SR > threshold
GT = GT == torch.max(GT)
# Inter = torch.sum((SR+GT)==2)
# DC = float(2*Inter)/(float(torch.sum(SR)+torch.sum(GT)) + 1e-6)
DC = binary.dc(SR.cpu().numpy(),GT.cpu().numpy())
return DC