Update README.md

README.md

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----
-license: apache-2.0
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+---
+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.