This should add the possibility to benchmark a segmentation model on the LIDC dataset by calculating the average Dice coefficient, Hausdorff distance, sensitivity and specificity.
I also fixed some flaws in the sensitivity and specificity calculation because we would otherwise divide by zero on certain occasions.
Description
Given a segmentation model, the method predicts each LIDC nodule segmentation mask and compares that with the ground truth mask using the aforementioned metrics.
Reference to official issue
This addresses #271.
Motivation and Context
We want to know how well the implemented models perform in order to improve them in a targeted way.
How Has This Been Tested?
I'd like to test it in the next days using a model that supports 3D convolutions.
CLA
[X] I have signed the CLA; if other committers are in the commit history, they have signed the CLA as well
This should add the possibility to benchmark a segmentation model on the LIDC dataset by calculating the average Dice coefficient, Hausdorff distance, sensitivity and specificity. I also fixed some flaws in the sensitivity and specificity calculation because we would otherwise divide by zero on certain occasions.
Description
Given a segmentation model, the method predicts each LIDC nodule segmentation mask and compares that with the ground truth mask using the aforementioned metrics.
Reference to official issue
This addresses #271.
Motivation and Context
We want to know how well the implemented models perform in order to improve them in a targeted way.
How Has This Been Tested?
I'd like to test it in the next days using a model that supports 3D convolutions.
CLA