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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
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
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
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 = 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