--- a
+++ b/Segmentation/metrics.py
@@ -0,0 +1,44 @@
+import numpy as np
+from keras import backend as K
+
+
+def dice_score(mask_gt, mask_pred):
+    """Computes soerensen-dice coefficient.
+
+    compute the soerensen-dice coefficient between the ground truth mask `mask_gt`
+    and the predicted mask `mask_pred`.
+
+    Args:
+    mask_gt: 3-dim Numpy array of type bool. The ground truth mask.
+    mask_pred: 3-dim Numpy array of type bool. The predicted mask.
+
+    Returns:
+    the dice coeffcient as float. If both masks are empty, the result is NaN.
+    """
+    volume_sum = mask_gt.sum() + mask_pred.sum()
+    if volume_sum == 0:
+        return np.NaN
+    volume_intersect = (mask_gt & mask_pred).sum()
+    return 2*volume_intersect / volume_sum 
+
+def dice_coef(y_true, y_pred, smooth=1):
+    """
+    Dice = (2*|X & Y|)/ (|X|+ |Y|)
+         =  2*sum(|A*B|)/(sum(A^2)+sum(B^2))
+    ref: https://arxiv.org/pdf/1606.04797v1.pdf
+    """
+    intersection = K.sum(K.abs(y_true * y_pred), axis=-1)
+    return (2. * intersection + smooth) / (K.sum(K.square(y_true),-1) + K.sum(K.square(y_pred),-1) + smooth)
+
+def dice_coef_loss(y_true, y_pred):
+    return 1-dice_coef(y_true, y_pred)
+
+def jacard_coef(y_true, y_pred):
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersection = K.sum(y_true_f * y_pred_f)
+    return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + 1.0)
+
+
+def jacard_coef_loss(y_true, y_pred):
+    return 1-jacard_coef(y_true, y_pred)
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