Update README.md

README.md

@@ -4,14 +4,14 @@
 [![Build Actions Status](https://github.com/dbouget/neuro_rads_prototype/workflows/Build/badge.svg)](https://github.com/dbouget/neuro_rads_prototype/actions)
 
 ## 1. Description
-Software to automatically segment tumors and their from pre-operative CTs and MRIs, and report them in a standardized manner.
+Software to automatically segment brain tumors from pre-operative MRI scans, compute their characteristics (e.g., volume, location), and generate a standardized report.
 
 The software was introduced in the article "Brain tumor preoperative surgery imaging: models and software solutions for
 segmentation and standardized reporting", which has been submitted to [Frontiers in Neurology](https://www.frontiersin.org/journals/neurology).
 
 ## 2. Softwares and usage
 An installer is provided for the three main Operating Systems: Windows (~v10, 64-bit), macOS (>= Catalina), and Ubuntu Linux (>= 18.04).
-The software can be downloaded from [here](https://github.com/SINTEFMedtek/GSI-RADS/releases) (see **Assets**). 
+The software can be downloaded from [here](https://github.com/dbouget/Raidionics/releases) (see **Assets**). 
 
 ### 2.1 Download and installation
 These steps are only needed to do once:
@@ -20,27 +20,32 @@ These steps are only needed to do once:
 3) Search for the software "Raidionics" and double click to run.
 
 ### 2.2 Usage  
-  1) Click 'Input MRI...' to select from your file explorer the MRI scan to process (unique file).  
+Two modes are proposed: (i) Single-use for processing one MRI scan at a time with the possibility to view and interact with the results, and (ii) Batch-mode for processing multiple MRI scans in a row, without any vizualisation.
+
+For the single use case:
+  1) Click 'Input MRI...' to select from your file explorer the MRI scan to process (unique file), preferably as nifti (*.nii, *.nii.gz).  
   1*) Alternatively, Click 'File > Import DICOM...' if you wish to process an MRI scan as a DICOM sequence.  
   2) Click 'Output destination' to choose a directory where to save the results.  
   3) (OPTIONAL) Click 'Input segmentation' to choose a tumor segmentation mask file, if nothing is provided the internal model with generate the segmentation automatically.  
-  4) Click 'Run diagnosis' to perform the analysis. The human-readable version of the results will be displayed directly in the interface.  
-
-  NOTE: The output folder is populated automatically with the following:  
-       * The diagnosis results in human-readable text (report.txt) and Excel-ready format (report.csv).  
-       * The automatic segmentation masks of the brain and the tumor in the original patient space (input_brain_mask.nii.gz and input_tumor_mask.nii.gz).  
-       * The cortical structures mask in original patient space for the different atlases used.  
-       * The input volume and tumor segmentation mask in MNI space in the sub-directory named \'registration\'.  
+  4) Select the tumor type from the drop-down menu, supported types are: (i) High-Grade Glioma (glioblastoma), (ii) Low-Grade Glioma, (iii) Meningioma, and (iv) Metastasis.
+  5) Click 'Run segmentation' to generate the brain tumor mask, or 'Run standard reporting' to perform the full analysis. The human-readable version of the results will be displayed directly in the interface.  
 
 ### 2.3 Computed features  
 The following features are automatically computed and reported to the user:
 - **Multifocality**: whether the tumor is multifocal or not, the total number of foci, and the largest minimum distance between two foci.  
 - **Volume**: total tumor volume in original patient space and MNI space (in ml).  
 - **Laterality**: tumor percentage in each hemisphere, and assessment of midline crossing.  
-- **Resectability**: expected residual volumes (in ml) and resection index.  
+- **Resectability**: expected residual volumes (in ml) and resection index (for high-grade gliomas only).  
 - **Cortical structures**: percentage of the tumor volume overlapping each structure from the MNI atlas, the Harvard-Oxford atlas, and Schaefer atlas (version 7 and 17).  
 - **Subcortical structures**: percentage of the tumor volume overlapping each structure from the BCB atlas. If no overlap, the minimum distance to the structure is provided (in mm).  
 
+### 2.4 Generated results  
+  The output folder is populated automatically with the following:  
+    * The diagnosis results in human-readable text (report.txt) and Excel-ready format (report.csv).  
+    * The automatic segmentation masks of the brain and the tumor in the original patient space (input_brain_mask.nii.gz and input_tumor_mask.nii.gz).  
+    * The cortical structures mask in original patient space for the different atlases used.  
+    * The input volume and tumor segmentation mask in MNI space in the sub-directory named \'registration\'.  
+
 ## 3. Source code usage
 
 ### 3.1 Installation
@@ -51,22 +56,19 @@ Use the requirements.txt file to create a virtual environment with the required
 > pip install -r ../requirements.txt  
 > deactivate  
 
-Then, to download the trained models locally, run the following:
-> source venv/bin/activate  
-> python setup.py  
-> deactivate  
-
 ### 3.2 Usage
 The command line input parameters are:
 * -g [--use_gui]: Must be set to 0 to disable the gui, otherwise 1.
 * -i [--input_filename]: Complete path to the MRI volume to process.
 * (optional) -s [--input_tumor_segmentation_filename]: Complete path to the corresponding tumor mask, to avoid re-segmentation.
 * -o [--output_folder]: Main destination directory. A unique timestamped folder will be created inside for each run.
 * -d [--gpu_id]: Number of the GPU to use for the segmentation task. Set the value to -1 to run on CPU.
+* -t [--task]: Process to perform, either segmentation or diagnosis (for generating the standardized report).
+* -m [--model_segmentation]: Name of the trained model to use (from the list of automatically downloadable models).
 
-To run directly from command line, without the use of the GUI, run the following:
+To run segmentation directly from command line, without the use of the GUI, run the following:
 > source venv/bin/activate  
-> python main.py -g 0 -i /path/to/volume/T1.nii.gz -o /path/to/output/ -d 0  
+> python main.py -g 0 -i /path/to/volume/T1.nii.gz -o /path/to/output/ -d 0 -t segmentation -m MRI_Meningioma 
 > deactivate
 
 ### How to cite