Image Segmentation with Topological Priors
This work addresses segmentation errors in fine-scale structures for applications like medical imaging, but it is incremental as it builds on existing methods.
The paper tackled image segmentation by incorporating topological priors into a UNet model, resulting in significantly better performance on accuracy metrics and topological correctness compared to simple segmentation.
Solving segmentation tasks with topological priors proved to make fewer errors in fine-scale structures. In this work, we use topological priors both before and during the deep neural network training procedure. We compared the results of the two approaches with simple segmentation on various accuracy metrics and the Betti number error, which is directly related to topological correctness, and discovered that incorporating topological information into the classical UNet model performed significantly better. We conducted experiments on the ISBI EM segmentation dataset.