Commit 60a6bd5b authored by Vaanathi Sundaresan's avatar Vaanathi Sundaresan
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Update README.md

parent e0effcfa
# Triplanar U-Net ensemble network (TrUE-Net) model
# Top 10 DL tool for MICCAI Brain Tumour Segmentation Challenge 2020
## Brain Tumour Segmentation with TrUE-Net tool
### Won 5th highest score in MICCAI Brain Tumour Segmentation Challenge 2020
## Preprint (to be updated)
#### Software versions used for truenet:
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#### Dependencies for prepare_truenet_data:
- FMRIB software library (FSL) 6.0
## TrUE-Net architecture:
<img
## TrUE-Net tumour segmentation results:
<img width="200"
src="images/Val_vis_res.png"
alt="Results on BraTS'20 validation data from three sample subjects."
/>
## TrUE-Net tumour segmentation architecture:
<img width="200"
src="images/Network_architecture.png"
alt="Proposed triplanar ensemble network architecture. (a) Input modalities in the axial plane along with manual segmentations for NCR/NET (blue), ET (yellow), ED (red) and TC (magenta), (b) the proposed network and (c) 3-layer deep U-Net blocks used in (b). Slices with 4 channels (input modalities) were provided to all U-nets."
/>
......@@ -37,7 +41,7 @@ truenet_tumseg <subcommand> --help (e.g. truenet train --help)
## Preprocessing and preparing data for truenet
We used FLAIR, T1, T1 CE and T2 as inputs for the model. We reoriented the images to the standard MNI space, performed skull-stripping FSL BET and registered the T1-weighted image to the FLAIR using linear rigid-body registration.
#### prepare_tumseg_data
## prepare_tumseg_data
```
Usage: prepare_tumseg_data <Base_modality_type> <output_basename> <FLAIR_image_name> <T1_image_name> <T1ce_image_name> <T2_image_name>
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