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Topology-Aware Uncertainty for Image Segmentation

This repository contains the implementation for our work "Topology-Aware Uncertainty for Image Segmentation", accepted to NeurIPS 2023.

The dmt-uncertainty.ipynb file contains simple code on how to use discrete Morse theory (DMT) to generate the Morse Skeleton (and the corresponding critical points and manifolds). It has some visualizations to understand the main code better.

USAGE

The current code is for 2D images. You will notice the word 'DRIVE' everywhere --- this is the dataset I used. You need to replace it with your dataset.

Dipha

Run the following commands. Need to run this only once.

cd dipha-graph-recon/
rm -rf build/
mkdir build
cd build
cmake ..
make

Training

  • Edit datalists/DRIVE/train.json with your hyperparameter values.
  • Command to run: CUDA_VISIBLE_DEVICES=7 python3 train.py --params ./datalists/DRIVE/train.json

Inference

  • Edit datalists/DRIVE/infer.json with your hyperparameter values.
  • Command to run: CUDA_VISIBLE_DEVICES=7 python3 infer.py --params ./datalists/DRIVE/infer.json

Acknowledgement

The code for computing DMT has been borrowed from here . I would like to thank them because it has formed the basis of this work. I modified their code to output the generated manifolds.

CITATION

If you found this work useful, please consider citing it as

@article{gupta2024topology,
  title={Topology-aware uncertainty for image segmentation},
  author={Gupta, Saumya and Zhang, Yikai and Hu, Xiaoling and Prasanna, Prateek and Chen, Chao},
  journal={Advances in Neural Information Processing Systems},
  volume={36},
  year={2024}
}