- change name

readme.md

@@ -1,32 +1,34 @@
-# HD-BET 
+# HD-BET
 
 This repository provides easy to use access to our recently published HD-BET brain extraction tool. HD-BET is the result
-of a joint project between the Department of Neuroradiology at the Heidelberg University Hospital and the 
+of a joint project between the Department of Neuroradiology at the Heidelberg University Hospital and the
 Division of Medical Image Computing at the German Cancer Research Center (DKFZ).
 
-If you are using HD-BET, please cite the following publication: 
+If you are using HD-BET, please cite the following publication:
 
-Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer HP, Heiland S, Wick W, 
-Bendszus M, Maier-Hein KH, Kickingereder P. Automated brain extraction of multi-sequence MRI using artificial neural 
+Isensee F, Schell M, Tursunova I, Brugnara G, Bonekamp D, Neuberger U, Wick A, Schlemmer HP, Heiland S, Wick W,
+Bendszus M, Maier-Hein KH, Kickingereder P. Automated brain extraction of multi-sequence MRI using artificial neural
 networks. Hum Brain Mapp. 2019; 1–13. https://doi.org/10.1002/hbm.24750
 
 Compared to other commonly used brain extraction tools, HD-BET has some significant advantages:
-- HD-BET was developed with MRI-data from a large multicentric clinical trial in adult brain tumor patients acquired 
-across 37 institutions in Europe and included a broad range of MR hardware and acquisition parameters, pathologies 
-or treatment-induced tissue alterations. We used 2/3 of data for training and validation and 1/3 for testing. 
-Moreover independent testing of HD-BET was performed in three public benchmark datasets (NFBS, LPBA40 and CC-359).
-- HD-BET was trained with precontrast T1-w, postcontrast T1-w, T2-w and FLAIR sequences. It can perform independent 
-brain extraction on various different MRI sequences and is not restricted to precontrast T1-weighted (T1-w) sequences.
- Other MRI sequences may work as well (just give it a try!)
-- HD-BET was designed to be robust with respect to brain tumors, lesions and resection cavities as well as different 
-MRI scanner hardware and acquisition parameters.
-- HD-BET outperformed five publicly available brain extraction algorithms (FSL BET, AFNI 3DSkullStrip, Brainsuite BSE, 
-ROBEX and BEaST) across all datasets and yielded median improvements of +1.33 to +2.63 points for the DICE 
-coefficient and -0.80 to -2.75 mm for the Hausdorff distance (Bonferroni-adjusted p<0.001). 
-- HD-BET is very fast on GPU with <10s run time per MRI sequence. Even on CPU it is not slower than other commonly 
-used tools.
-
-## Installation Instructions 
+
+- HD-BET was developed with MRI-data from a large multicentric clinical trial in adult brain tumor patients acquired
+  across 37 institutions in Europe and included a broad range of MR hardware and acquisition parameters, pathologies
+  or treatment-induced tissue alterations. We used 2/3 of data for training and validation and 1/3 for testing.
+  Moreover independent testing of HD-BET was performed in three public benchmark datasets (NFBS, LPBA40 and CC-359).
+- HD-BET was trained with precontrast T1-w, postcontrast T1-w, T2-w and FLAIR sequences. It can perform independent
+  brain extraction on various different MRI sequences and is not restricted to precontrast T1-weighted (T1-w) sequences.
+  Other MRI sequences may work as well (just give it a try!)
+- HD-BET was designed to be robust with respect to brain tumors, lesions and resection cavities as well as different
+  MRI scanner hardware and acquisition parameters.
+- HD-BET outperformed five publicly available brain extraction algorithms (FSL BET, AFNI 3DSkullStrip, Brainsuite BSE,
+  ROBEX and BEaST) across all datasets and yielded median improvements of +1.33 to +2.63 points for the DICE
+  coefficient and -0.80 to -2.75 mm for the Hausdorff distance (Bonferroni-adjusted p<0.001).
+- HD-BET is very fast on GPU with <10s run time per MRI sequence. Even on CPU it is not slower than other commonly
+  used tools.
+
+## Installation Instructions
+
 Note that you need to have a python3 installation for HD-BET to work. Please also make sure to install HD-BET with the
 correct pip version (the one that is connected to python3). You can verify this using the `--version` command:
 
@@ -38,67 +40,69 @@ pip 20.0.2 from /home/fabian/dl_venv/lib/python3.6/site-packages/pip (python 3.6
 If it does not show python 3.X, you can try pip3. If that also does not work you probably need to install python3 first.
 
 Once python 3 and pip are set up correctly, run the following commands to install HD-BET:
-1) Clone this repository:
-    ```bash
-    git clone https://github.com/MIC-DKFZ/HD-BET
-    ```
-2) Go into the repository (the folder with the setup.py file) and install:
-    ```
-    cd HD-BET
-    pip install -e .
-    ```
-3) Per default, model parameters will be downloaded to ~/hd-bet_params. If you wish to use a different folder, open 
-HD_BET/paths.py in a text editor and modify ```folder_with_parameter_files```
-
-
-## How to use it 
-
-Using HD_BET is straightforward. You can use it in any terminal on your linux system. The hd-bet command was installed 
-automatically. We provide CPU as well as GPU support. Running on GPU is a lot faster though 
-and should always be preferred. Here is a minimalistic example of how you can use HD-BET (you need to be in the HD_BET 
+
+1. Clone this repository:
+   ```bash
+   git clone https://github.com/MIC-DKFZ/HD-BET
+   ```
+2. Go into the repository (the folder with the setup.py file) and install:
+   ```
+   cd HD-BET
+   pip install -e .
+   ```
+3. Per default, model parameters will be downloaded to ~/hd-bet_params. If you wish to use a different folder, open
+   HD_BET/paths.py in a text editor and modify `folder_with_parameter_files`
+
+## How to use it
+
+Using HD_BET is straightforward. You can use it in any terminal on your linux system. The hd-bet command was installed
+automatically. We provide CPU as well as GPU support. Running on GPU is a lot faster though
+and should always be preferred. Here is a minimalistic example of how you can use HD-BET (you need to be in the HD_BET
 directory)
 
 ```bash
 hd-bet -i INPUT_FILENAME
 ```
 
-INPUT_FILENAME must be a nifti (.nii.gz) file containing 3D MRI image data. 4D image sequences are not supported 
-(however can be splitted upfront into the individual temporal volumes using fslsplit<sup>1</sup>). 
+INPUT_FILENAME must be a nifti (.nii.gz) file containing 3D MRI image data. 4D image sequences are not supported
+(however can be splitted upfront into the individual temporal volumes using fslsplit<sup>1</sup>).
 INPUT_FILENAME can be either a pre- or postcontrast T1-w, T2-w or FLAIR MRI sequence. Other modalities might work as well.
-Input images must match the orientation of standard MNI152 template! Use fslreorient2std <sup>2</sup> upfront to ensure 
+Input images must match the orientation of standard MNI152 template! Use fslreorient2std <sup>2</sup> upfront to ensure
 that this is the case.
 
-By default, HD-BET will run in GPU mode, use the parameters of all five models (which originate from a five-fold 
+By default, HD-BET will run in GPU mode, use the parameters of all five models (which originate from a five-fold
 cross-validation), use test time data augmentation by mirroring along all axes and not do any postprocessing.
 
-For batch processing it is faster to process an entire folder at once as this will mitigate the overhead of loading 
+For batch processing it is faster to process an entire folder at once as this will mitigate the overhead of loading
 and initializing the model for each case:
 
 ```bash
 hd-bet -i INPUT_FOLDER -o OUTPUT_FOLDER
 ```
 
-The above command will look for all nifti files (*.nii.gz) in the INPUT_FOLDER and save the brain masks under the same name
+The above command will look for all nifti files (\*.nii.gz) in the INPUT_FOLDER and save the brain masks under the same name
 in OUTPUT_FOLDER.
 
-### GPU is nice, but I don't have one of those... What now? 
+### GPU is nice, but I don't have one of those... What now?
 
-HD-BET has CPU support. Running on CPU takes a lot longer though and you will need quite a bit of RAM. To run on CPU, 
+HD-BET has CPU support. Running on CPU takes a lot longer though and you will need quite a bit of RAM. To run on CPU,
 we recommend you use the following command:
 
 ```bash
 hd-bet -i INPUT_FOLDER -o OUTPUT_FOLDER -device cpu -mode fast -tta 0
 ```
+
 This works of course also with just an input file:
 
 ```bash
 hd-bet -i INPUT_FILENAME -device cpu -mode fast -tta 0
 ```
 
-The options *-mode fast* and *-tta 0* will disable test time data augmentation (speedup of 8x) and use only one model instead of an ensemble of five models 
+The options _-mode fast_ and _-tta 0_ will disable test time data augmentation (speedup of 8x) and use only one model instead of an ensemble of five models
 for the prediction.
 
 ### More options:
+
 For more information, please refer to the help functionality:
 
 ```bash
@@ -107,19 +111,18 @@ hd-bet --help
 
 ## FAQ
 
-1) **How much GPU memory do I need to run HD-BET?**  
-We ran all our experiments on NVIDIA Titan X GPUs with 12 GB memory. For inference you will need less, but since 
-inference in implemented by exploiting the fully convolutional nature of CNNs the amount of memory required depends on 
-your image. Typical image should run with less than 4 GB of GPU memory consumption. If you run into out of memory
-problems please check the following: 1) Make sure the voxel spacing of your data is correct and 2) Ensure your MRI 
-image only contains the head region
-2) **Will you provide the training code as well?**  
-No. The training code is tightly wound around the data which we cannot make public.
-3) **What run time can I expect on CPU/GPU?**  
-This depends on your MRI image size. Typical run times (preprocessing, postprocessing and resampling included) are just
- a couple of seconds for GPU and about 2 Minutes on CPU (using ```-tta 0 -mode fast```)
-
+1. **How much GPU memory do I need to run HD-BET?**  
+   We ran all our experiments on NVIDIA Titan X GPUs with 12 GB memory. For inference you will need less, but since
+   inference in implemented by exploiting the fully convolutional nature of CNNs the amount of memory required depends on
+   your image. Typical image should run with less than 4 GB of GPU memory consumption. If you run into out of memory
+   problems please check the following: 1) Make sure the voxel spacing of your data is correct and 2) Ensure your MRI
+   image only contains the head region
+2. **Will you provide the training code as well?**  
+   No. The training code is tightly wound around the data which we cannot make public.
+3. **What run time can I expect on CPU/GPU?**  
+   This depends on your MRI image size. Typical run times (preprocessing, postprocessing and resampling included) are just
+   a couple of seconds for GPU and about 2 Minutes on CPU (using `-tta 0 -mode fast`)
 
 <sup>1</sup>https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Fslutils
 
-<sup>2</sup>https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained
+<sup>2</sup>https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Orientation%20Explained