README.md

---
license: apache-2.0
pipeline_tag: image-segmentation
tags:
- medical
- vessel segmentation
- small vessel segmentation
- UNet
- UNetMSS
- Deep Supervision
- 3D
- DS6
- 7T MRA-ToF
- MRA
- TOF
- MRI
- 7T
- brain MRI
- vessels
library_name: pytorch
---
# DS6_UNetMSS3D_woDeform

Blood vessels of the brain provide the human brain with the required nutrients and oxygen. As a vulnerable part of the cerebral blood supply, pathology of small vessels can cause serious problems such as Cerebral Small Vessel Diseases (CSVD). It has also been shown that CSVD is related to neurodegeneration, such as Alzheimer’s disease. With the advancement of 7 Tesla MRI systems, higher spatial image resolution can be achieved, enabling the depiction of very small vessels in the brain. Non-Deep Learning-based approaches for vessel segmentation, e.g., Frangi’s vessel enhancement with subsequent thresholding, are capable of segmenting medium to large vessels but often fail to segment small vessels. The sensitivity of these methods to small vessels can be increased by extensive parameter tuning or by manual corrections, albeit making them time-consuming, laborious, and not feasible for larger datasets. This paper proposes a deep learning architecture to automatically segment small vessels in 7 Tesla 3D Time-of-Flight (ToF) Magnetic Resonance Angiography (MRA) data. The algorithm was trained and evaluated on a small imperfect semi-automatically segmented dataset of only 11 subjects; using six for training, two for validation, and three for testing. The deep learning model based on U-Net Multi-Scale Supervision was trained using the training subset and was made equivariant to elastic deformations in a self-supervised manner using deformation-aware learning to improve the generalisation performance. The proposed technique was evaluated quantitatively and qualitatively against the test set and achieved a Dice score of 80.44 ± 0.83. Furthermore, the result of the proposed method was compared against a selected manually segmented region (62.07 resultant Dice) and has shown a considerable improvement (18.98%) with deformation-aware learning.

## Model Details

It was introduced in [DS6, Deformation-Aware Semi-Supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data](https://doi.org/10.3390/jimaging8100259) by Soumick Chatterjee, Kartik Prabhu, Mahantesh Pattadkal, Gerda Bortsova, Chompunuch Sarasaen, Florian Dubost, Hendrik Mattern, Marleen de Bruijne, Oliver Speck, Andreas Nürnberger. [ArXiv preprint](https://arxiv.org/abs/2006.10802)

### Model Description

- **Model type:** UNet Multi-scale Supervision (UNet-MSS) 3D
- **Task:** Vessel segmentation in 7T MRA-ToF volumes
- **Training dataset:** In-house 7T ToF-MRAs, details can be found in Section 4 of the published paper.
- **Training type:** Trained without deformation-aware learning (Baseline method)

### Model Sources

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

- **Repository:** https://github.com/soumickmj/DS6
- **Paper:** https://doi.org/10.3390/jimaging8100259
- **Preprint:** https://arxiv.org/abs/2006.10802

## 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{chatterjee2022ds6,
  title={Ds6, deformation-aware semi-supervised learning: Application to small vessel segmentation with noisy training data},
  author={Chatterjee, Soumick and Prabhu, Kartik and Pattadkal, Mahantesh and Bortsova, Gerda and Sarasaen, Chompunuch and Dubost, Florian and Mattern, Hendrik and de Bruijne, Marleen and Speck, Oliver and N{\"u}rnberger, Andreas},
  journal={Journal of Imaging},
  volume={8},
  number={10},
  pages={259},
  year={2022},
  publisher={MDPI}
}
```

**APA:**

Chatterjee, S., Prabhu, K., Pattadkal, M., Bortsova, G., Sarasaen, C., Dubost, F., ... & Nürnberger, A. (2022). Ds6, deformation-aware semi-supervised learning: Application to small vessel segmentation with noisy training data. Journal of Imaging, 8(10), 259.