Hi @benolmbrt, thanks for your reporting.

[RY-97]

          Hi @benolmbrt, thanks for your reporting.

The behavior you're seeing with the DiceMetric is likely due to a misunderstanding of the ignore_empty parameter's functionality.
When the ignore_empty option is set to True, it's supposed to set a NaN value for cases where the ground truth is empty. However, this does not mean that a NaN will be returned for such classes when computing the metric. Instead, these cases are ignored during the calculation process.Thus, even if some classes are ignored (due to being absent in the ground truth when ignore_empty=True), the metric computation does not return NaN for these classes. Instead, it computes the metric over the non-ignored classes only.
If the DiceMetric were to return a NaN for an ignored class, this would also affect the computed average Dice score (since any mathematical operation with NaN results in NaN), rendering the metric not well-defined for batches that include at least one empty class in the ground truth.

I hope this helps and let me know if you have any other questions!
Thanks.

Originally posted by @KumoLiu in #7504 (comment)

[RY-97]

Why does this line of code produce "nan" when calculating, can you help me solve it?

[KumoLiu]

Hi @RY-97, please follow the example to generate Dice. Pay attention to the indentation.

        dice_metric = DiceMetric(include_background=True, reduction="mean")

        for val_data in val_loader:
            val_outputs = model(val_data["img"])
            val_outputs = [postprocessing_transform(i) for i in decollate_batch(val_outputs)]
            # compute metric for current iteration
            dice_metric(y_pred=val_outputs, y=val_data["seg"])  # callable to add metric to the buffer

        # aggregate the final mean dice result
        metric = dice_metric.aggregate().item()

        # reset the status for next computation round
        dice_metric.reset()

For more details you can refer to the tutorial repo:
https://github.com/Project-MONAI/tutorials/blob/main/3d_segmentation/brats_segmentation_3d.ipynb

Hope it helps, thanks.