I have a segmentation which outputs only one channel image (2 class segmentation). I have used dice score for most of the time, but now higher powers in my team want me to expand evaluation metrics for segmentation model (if it's even possible). I have done some research and as far as right now I have found mainly that everybody uses dice score, and sometimes pixel to pixel binary accuracy, but for the latter seems not the best idea.

If anybody knows something exciting or useful, I'd be glad to hear from them.


1 Answer 1


Typical metrics used with segmentation problems are Recall, Precision and the F1 Score (similar or the same as the Dice score depending on the definition used). These can be evaluated per class or for all classes together, commonly referred to as micro and macro averages.

Taking it further, you may wish to have a metric more robust to changes in the threshold. Here the Area under the Curve (AUC) metric is commonly used.

For a more sophisticated analysis you may also be interested in perceptual losses. These quantify how similar an image looks as perceived by people. This is particularly useful if say the shape of the prediction is important but small shifts or scaling does not matter. Have a look at SSIM and LPIPS losses for more information on these.

TorchMetrics may be a good place to look implementations and available metrics.


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