What is Dice loss in image segmentation?

What is Dice loss in image segmentation?

Dice loss originates from Sørensen–Dice coefficient, which is a statistic developed in 1940s to gauge the similarity between two samples [Wikipedia]. It was brought to computer vision community by Milletari et al. in 2016 for 3D medical image segmentation.

What is generalized dice loss?

Generalized Dice loss is the multi-class extension of Dice loss where the weight of each class is inversely proportional to the square of label frequencies. Focal Tversky loss applies the concept of focal loss to focus on hard cases with low probabilities.

What is a good Dice coefficient?

Dice coefficient shouldn’t be greater than 1. A dice coefficient usually ranges from 0 to 1. If you are getting a coefficient greater than 1, maybe you need to check your implementation.

How are dice scores calculated?

Simply put, the Dice Coefficient is 2 * the Area of Overlap divided by the total number of pixels in both images.

How to calculate dice loss for multi class segmentation?

Now in this dataset you will only ever have two classes present in the image. This means that, following your dice loss, 9 of the weights will be 1./ (0. + eps) = large and so for every image we are strongly penalising all 9 non-present classes.

What happens to the weights after a dice loss?

This means that, following your dice loss, 9 of the weights will be 1./ (0. + eps) = large and so for every image we are strongly penalising all 9 non-present classes. An evidently strong local minima the network wants to find in this situation is to predict everything as a background class.

When to use generalized dice loss in keras?

Plus I believe it would be usefull to the keras community to have a generalised dice loss implementation, as it seems to be used in most of recent semantic segmentation tasks (at least in the medical image community). PS: it seems odd to me how the weights are defined; I get values around 10^-10.

Which is the best loss function for segmentation?

Use weighted Dice loss and weighted cross entropy loss. Dice loss is very good for segmentation. The weights you can start off with should be the class frequencies inversed i.e take a sample of say 50-100, find the mean number of pixels belonging to each class and make that classes weight 1/mean.