Regarding 2:

Empty reference and non-empty prediction

The 100 is a bit weird given the the reference is empty so a division by an empty array should be Infinity, but I understand that we need to show that this is oversegmentation so you set 100 here

The 100 is a bit weird given the reference is empty so a division by an empty array should be Infinity,

Yes, you are right; without this condition, the RVE would be Infinity. Which would complicate aggregation across subjects, though.

but I understand that we need to show that this is oversegmentation so you set 100 here

Yeah, I choose 100 to be the opposite of -100 (which results from non-empty reference and empty prediction).

Hey @naga-karthik! I believe the PR can now be beta-tested.

I implemented the following points based on our discussions:

• the script now accepts folders with multiple nii files as inputs (the compatibility with a single nii file is still preserved!)
• the output is saved as CSV (instead of JSON) --> easier to manipulate and plot
• the script also outputs a second CSV with mean +- std metrics
• output CSV files use full metric names (instead of metric abbreviations)

I also updated the handling of the case when both the reference and prediction are empty in a23b81b.

So I am trying out the installation of MetricsReloaded and followed the commands mentioned here. However, I get a pip dependency resolver error post-installation

Is it something you also had? How did you solve this? @valosekj

So I am trying out the installation of MetricsReloaded and followed the commands mentioned here. However, I get a pip dependency resolver error post-installation

I just tried the commands and the installation completed successfully on my end. We can check it together in person.

CI is passing and I self-reviewed the code --> merging