---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
- probabilistic unet
- 3D
- PULASki
- vessel segmentation
- 7T MRA-ToF
- MRA
- TOF
- MRI
- 7T
- Conditional VAE
- distribution distance
library_name: pytorch
---
# PULASki_ProbUNet3D_Sinkhorn_VSeg

In the domain of medical imaging, many supervised learning based methods for segmentation face several challenges such as high variability in annotations from multiple experts, paucity of labelled data and class imbalanced datasets.  These issues may result in segmentations that lack the requisite precision for clinical analysis and can be misleadingly overconfident without associated uncertainty quantification.  

We proposed the PULASki as a computationally efficient generative tool for biomedical image segmentation that accurately captures variability in expert annotations, even in small datasets.  Our approach makes use of an improved loss function based on statistical distances in a conditional variational autoencoder structure (Probabilistic UNet), which improves learning of the conditional decoder compared to the standard cross-entropy particularly in class imbalanced problems.

## Model Details

It was introduced in [PULASki: Learning inter-rater variability using statistical distances to improve
probabilistic segmentation](https://arxiv.org/abs/2312.15686) by Soumick Chatterjee, Franziska Gaidzik, Alessandro Sciarra, Hendrik Mattern, Gábor Janiga, Oliver Speck, Andreas Nürnberger, Sahani Pathiraja.

### Model Description

- **Developed by:** Dr Soumick Chatterjee
- **Model type:** PULASki 3D Probabilistic UNet, trained with Sinkhorn loss
- **Task:** Probabilistic vessel segmentation in 7T MRA-ToF volumes 
- **Training dataset:** 7T MRA-ToF volumes, details mentioned in Sec. 4.1 of https://arxiv.org/pdf/2312.15686

### Model Sources

<!-- Provide the basic links for the model. -->

- **Repository:** https://github.com/soumickmj/PULASki
- **Paper:** https://arxiv.org/abs/2312.15686

## Citation

<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->

If you use this approach in your research or use codes from this repository or these weights, please cite the following in your publications:

**BibTeX:**

```bibtex
@article{chatterjee2023pulaski,
  title={PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation},
  author={Chatterjee, Soumick and Gaidzik, Franziska and Sciarra, Alessandro and Mattern, Hendrik and Janiga, G{\'a}bor and Speck, Oliver and N{\"u}rnberger, Andreas and Pathiraja, Sahani},
  journal={arXiv preprint arXiv:2312.15686},
  year={2023}
}

```

**APA:**

Chatterjee, S., Gaidzik, F., Sciarra, A., Mattern, H., Janiga, G., Speck, O., Nuernberger, A., & Pathiraja, S. (2023). PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation. arXiv preprint arXiv:2312.15686.