[Python] Good performance with Accuracy but not with Dice loss in Image Segmentation

I'm doing image segmentation with U-Net like architecture on Tensorflow w/Keras but I'm new in Deep Learning.
I've got this dataset with the following set shapes:

• Train : X : (175250, 30, 30, 6) Y: (175250, 30, 30)
• Validation: X: (29200, 30, 30, 6) Y: (29200, 30, 30)
• Test : X: (29200, 30, 30, 6) Y: (29200, 30, 30)

Got some examples of these images and each channel, further down.

--> with 20% positive examples and 80% negative examples equally in each set

I run some series but for the best filter combination it made the plot for BCE with good accuracy:

The plot for custom functions, Dice_Loss by Dice_Coeff:



And some images generated from the best model trained with test images:



The problem is when I change to dice loss and coefficient, there aren´t good predictions as we seen in the image plot and now it isn´t in the image prediction as we may see.

Why it performs so badly in the dice loss? What other function do you recommend?

My dice loss and coefficient function:

def dice_coeff(y_true, y_pred, smooth=1):
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_loss(y_true, y_pred):
return 1-dice_coeff(y_true, y_pred)​

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