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
- CamSVD
- small vessel disease
- SVD
- lacunar strokes
library_name: pytorch
---
# SMILEUHURA_DS6_CamSVD_UNetMSS3D_wDeform

The main model from DS6 (UNetMSS + Deformation-aware learning), first trained on 7T ToF-MRAs of healthy subjects from the SMILE-UHURA dataset (https://doi.org/10.7303/syn47164761) and was then fine-tuned on the Cambridge 7T Cerebral Small Vessel Disease (CamSVD) dataset containing data from subjects with Lacunar strokes with SVD, non-lacunar strokes without SVD, and healthy controls.

This study aimed to develop a semi-automatic pipeline for quantifying the 3D morphology of lenticulostriate arteries (LSAs) in small vessel disease (SVD) patients using 7 Tesla Time-of-Flight magnetic resonance angiography (TOF-MRA). The motivation behind the work stems from the limitations of manual 2D analysis methods, which fail to capture the full complexity of LSA morphology. To address this, the authors fine-tuned the deep learning-based DS6 model for vessel segmentation and compared its performance to the classical Frangi filter-based MSFDF method. The DS6 model outperformed MSFDF with a mean Dice similarity coefficient of 0.814±0.029, showing superior sensitivity in detecting weaker LSA branches. Quantifying the segmented LSAs revealed various metrics, including the number of branches, length, and tortuosity. This deep learning-based pipeline offers a more accurate and efficient approach for assessing the 3D structure of LSAs, facilitating future research into the pathophysiology of SVD.
## 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)

The model architecture is the same as the original paper, but it was first trained on the SMILE-UHURA dataset, and then fine-tuned on the Cambridge 7T Cerebral Small Vessel Disease (CamSVD) dataset. While fine-tuning, additional 3D Spatial Dropout layers were added within the convolutional blocks. 
The results of this fine-tuned model are presented in [A Deep Learning Based Pipeline for the Analysis of the 3D Morphology of Lenticulostriate Arteries in Cerebral Small Vessel Disease from 7 Tesla Time-of-Flight MRA](ComingSoon!)

### Model Description

- **Model type:** UNet Multi-scale Supervision (UNet-MSS) 3D
- **Task:** Vessel segmentation in 7T MRA-ToF volumes
- **Initial training dataset:** 7T ToF-MRAs from the vessel segmentation challenge: SMILE-UHURA (https://doi.org/10.7303/syn47164761) 
- **Fine-tuning dataset:** 7T ToF-MRAs from the Cambridge 7T Cerebral Small Vessel Disease (CamSVD) study, consisting of healthy controls, and subjects with lacunar strokes with SVD and non-lacunar strokes without SVD.
- **Training type:** Trained with deformation-aware learning 

### Model Sources

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

DS6 (vessel segmentation):
- **Repository:** https://github.com/soumickmj/DS6
- **Paper:** https://doi.org/10.3390/jimaging8100259
- **Preprint:** https://arxiv.org/abs/2006.10802
  
This extended study (post-vessel segmentation):
- **Repository:** ComingSoon! 
- **Preprint:** ComingSoon!

## 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 all the following in your publications:

**BibTeX:**

DS6:
```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.