Awesome

imageSeg-2.5_topo

This is another implementation of topological loss, containing both 2D and 2.5D version.

Tips for training with topological loss

The computation of persistent homology is run on CPU and kind of slow. So in order to make it work, actually we adopted lots of engineering tricks during training:

1. Use pretrained models: pretrain the model without topo loss until obtaining reasonable likelihood maps and then finetune with topo loss to fix specific positions.

2. Small patches: apply topo loss on small patches instead of whole images to reduce computation cost.

3. Reduce unnecessary computation: during the finetune step, skip the topo loss computation if the likelihood map is already acceptable (checking by betti number).