Commit ef455a23 authored by Vaanathi Sundaresan's avatar Vaanathi Sundaresan
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Update README.md

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# Top 10 DL tool for MICCAI Brain Tumour Segmentation Challenge 2020 # Top 10 DL tool for MICCAI Brain Tumour Segmentation Challenge 2020
## Brain Tumour Segmentation with TrUE-Net tool ## Brain Tumour Segmentation with TrUE-Net tool
## Preprint (to be updated)
#### Software versions used for truenet: #### Software versions used for truenet:
- Python > 3.6 - Python > 3.6
- PyTorch=1.5.0 - PyTorch=1.5.0
...@@ -10,6 +8,15 @@ ...@@ -10,6 +8,15 @@
#### Dependencies for prepare_truenet_data: #### Dependencies for prepare_truenet_data:
- FMRIB software library (FSL) 6.0 - FMRIB software library (FSL) 6.0
## Papers to cite
- Sundaresan V., Griffanti L., Jenkinson M. (2021) Brain Tumour Segmentation Using a Triplanar Ensemble of U-Nets on MR Images. In: Crimi A., Bakas S. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science, vol 12658. Springer, Cham. [DOI: https://doi.org/10.1007/978-3-030-72084-1_31]
- Preprint on arXiv to be updated soon.
- Sundaresan, V., Zamboni, G., Rothwell, P. M., Jenkinson, M., & Griffanti, L. (2020). Triplanar ensemble U-Net model for white matter hyperintensities segmentation on MR images. BioRxiv. [DOI: https://doi.org/10.1101/2020.07.24.219485]
Also refer to the following paper to know more about truenet fine-tuning:
- Sundaresan, V., Zamboni, G., Dinsdale, N. K., Rothwell, P. M., Griffanti, L., & Jenkinson, M. (2021). Comparison of domain adaptation techniques for white matter hyperintensity segmentation in brain MR images. bioRxiv. [DOI: https://doi.org/10.1101/2021.03.12.435171]
## TrUE-Net tumour segmentation results: ## TrUE-Net tumour segmentation results:
<img width="750" <img width="750"
src="images/Val_vis_res.png" src="images/Val_vis_res.png"
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