Merge pull request #4 from raidionics/docker_update

Models download from Github only, new Docker image backend.

LICENSE

@@ -0,0 +1,26 @@
+BSD 2-Clause License
+
+Copyright (c) 2021, dbouget
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this
+   list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice,
+   this list of conditions and the following disclaimer in the documentation
+   and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+

README.md

@@ -1,20 +1,33 @@
-# Raidionics-Slicer
+<div align="center">
+<h1 align="center">Raidionics plugin (3D Slicer)</h1>
+<h3 align="center">Open plugin for AI-based segmentation and standardized reporting for neuro and mediastinal applications</h3>
+
+[![License](https://img.shields.io/badge/License-BSD%202--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause)
+[![Paper](https://zenodo.org/badge/DOI/10.1038/s41598-023-42048-7.svg)](https://doi.org/10.1038/s41598-023-42048-7)
+[![Paper](https://zenodo.org/badge/DOI/10.3389/fneur.2022.932219.svg)](https://www.frontiersin.org/articles/10.3389/fneur.2022.932219/full)
+
+
 Plugin for 3D Slicer to use the segmentation models and clinical reporting techniques (RADS) packaged in Raidionics.
+A paper presenting the software and some benchmarks has been published in [Scientific Reports](https://doi.org/10.1038/s41598-023-42048-7).  
 The plugin was first introduced in the article _"Brain tumor preoperative surgery imaging: models and software solutions for
 segmentation and standardized reporting"_, published in [Frontiers in Neurology](https://www.frontiersin.org/articles/10.3389/fneur.2022.932219/full).  
 
 <p align="center">
 <img src="Raidionics/Raidionics/Resources/Icons/Raidionics-Slicer.gif" width="85%">
 </p>
+</div>
 
-<details open>
-<summary>
 
-## Installation
-</summary>
+## [Installation](https://github.com/raidionics/Raidionics-Slicer#installation)
+
+The plugin has been tested with the stable release 5.6.1 of 3D Slicer, and the upcoming release 5.7.0.  
+A [step-by-step video](https://www.youtube.com/watch?v=NStMzLcj1WE) for installing the plugin and running a segmentation model for the first time is available.
 
-The plugin has been tested with the stable release 5.2.2 of 3D Slicer.
+<details>
+<summary>
 
+### [Step-by-step](https://github.com/raidionics/Raidionics-Slicer#step-by-step)
+</summary>
 * Download 3D Slicer for your running operating system from [here](https://download.slicer.org/).
 
 * Download the plugin source code through either:  
@@ -25,14 +38,14 @@ The plugin has been tested with the stable release 5.2.2 of 3D Slicer.
 * Download and install Docker (see [below](https://github.com/raidionics/Raidionics-Slicer#docker-setup--)).  
   The necessary _Raidionics_ Docker image should be collected automatically when downloading a model for the first time.
   Please do the following if it did not happen correctly:
-     > docker pull dbouget/raidionics-rads:v1.1
+     > docker pull dbouget/raidionics-rads:v1.1-py38-cpu
 
 * Load the plugin into 3D Slicer:  
 	∘ All Modules > Extension Wizard.  
 	∘ Developer Tools > Extension Wizard.  
-	∘ Select Extension > point to the folder (second Raidionics) and add it to the path (tick the small box at the bottom).
+	∘ Select Extension > point to the first Raidionics subfolder (inside Raidionics-Slicer) and add it to the path (tick the small box at the bottom).
 
-* Restart 3D Slicer to setup Python environment.
+* :warning: Restarting 3D Slicer to setup Python environment might be necessary on some occasions.
 
 * Raidionics will appear in the list of modules inside the _Machine Learning_ category
 
@@ -41,7 +54,8 @@ The plugin has been tested with the stable release 5.2.2 of 3D Slicer.
 <details open>
 <summary>
 
-## How to cite </summary>
+## [How to cite](https://github.com/raidionics/Raidionics-Slicer#how-to-cite) 
+</summary>
 If you are using Raidionics-Slicer in your research, please use the following citation:  
 
 ```
@@ -61,27 +75,30 @@ If you are using Raidionics-Slicer in your research, please use the following ci
 <details>
 <summary>
 
-## Methodological background </summary>
+## [Methodological background](https://github.com/raidionics/Raidionics-Slicer#methodological-background) 
+</summary>
 
 More information about the different models provided and architectures used can be accessed from the below-listed publications.  
 
 ### Neuro  
 * AGU-Net neural network architecture => [Meningioma Segmentation in T1-Weighted MRI Leveraging Global Context and Attention Mechanisms](https://www.frontiersin.org/articles/10.3389/fradi.2021.711514/full)
-
 * Standardized reporting and Data System => [Glioblastoma Surgery Imaging—Reporting and Data System: Standardized Reporting of Tumor Volume, Location, and Resectability Based on Automated Segmentations ](https://www.mdpi.com/2072-6694/13/12/2854)
-
-* Segmentation performance => [Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task ](https://www.mdpi.com/2072-6694/13/18/4674)
+* Preoperative GBM segmentation performance => [Glioblastoma Surgery Imaging–Reporting and Data System: Validation and Performance of the Automated Segmentation Task ](https://www.mdpi.com/2072-6694/13/18/4674)
+* Postoperative GBM segmentation performance => [Segmentation of glioblastomas in early post-operative multi-modal MRI with deep neural networks](https://www.nature.com/articles/s41598-023-45456-x)
+* Preoperative CNS segmentation performance => [Preoperative brain tumor imaging: models and software for segmentation and standardized reporting](https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2022.932219/full)
 
 ### Mediastinum
 * Mediastinum organs segmentation => [Semantic segmentation and detection of mediastinal lymph nodes and anatomical structures in CT data for lung cancer staging](https://link.springer.com/article/10.1007/s11548-019-01948-8)  
 * Lymph nodes segmentation => [Mediastinal lymph nodes segmentation using 3D convolutional neural network ensembles and anatomical priors guiding](https://www.tandfonline.com/doi/pdf/10.1080/21681163.2022.2043778)
+* Airways segmentation => [AeroPath: An airway segmentation benchmark dataset with challenging pathology](https://arxiv.org/abs/2311.01138)
 
 </details>
 
 <details>
 <summary>
 
-## Docker setup  </summary>  
+## [Docker setup](https://github.com/raidionics/Raidionics-Slicer#docker-setup)
+</summary>  
 A proper Docker setup is **mandatory** since all processing is performed within
 a Docker image. 3D Slicer is only used for its graphical user interface.
 
@@ -107,7 +124,7 @@ All images will be automatically downloaded upon model selection, which might
 take some minutes while the 3D Slicer interface won't be responding.  
 
 * The main Docker image can also be downloaded manually by:   
-    > docker pull dbouget/raidionics-rads:v1.1
+    > docker pull dbouget/raidionics-rads:v1.1-py38-cpu
 
 * When you execute for the first time, you might get a pop-up from Docker asking to allow
 the sharing of a `.raidonics-slicer/` directory, accept!

Raidionics/Raidionics/src/RaidionicsLogic.py

@@ -275,7 +275,7 @@ def executeDocker(self, dockerName, modelName, dataPath, iodict, inputs, outputs
         else:
             widgetPresent = False
 
-        dataPath = '/home/ubuntu/resources'
+        dataPath = '/workspace/resources'
 
         # Cleaning input/output folders for every run
         if os.path.exists(SharedResources.getInstance().data_path):
@@ -383,14 +383,16 @@ def executeDocker(self, dockerName, modelName, dataPath, iodict, inputs, outputs
 
         cmd = list()
         cmd.append(self.dockerPath)
-        cmd.extend(('run', '-t', '-v'))
+        cmd.extend(('run', '-t'))
         # if self.use_gpu:
         #     cmd.append(' --runtime=nvidia ')
-        #cmd.append(TMP_PATH + ':' + dataPath)
+        cmd.append('--user')
+        cmd.append(str(os.getuid()))
+        cmd.append('-v')
         cmd.append(SharedResources.getInstance().resources_path + ':' + dataPath)
         cmd.append(dockerName)
         cmd.append('-c')
-        cmd.append('/home/ubuntu/resources/data/rads_config.ini')
+        cmd.append('/workspace/resources/data/rads_config.ini')
         cmd.append('-v')
         cmd.append('debug')
 

Raidionics/Raidionics/src/utils/backend_utilities.py

@@ -31,13 +31,13 @@ def generate_backend_config(input_folder: str, parameters, logic_target_space: s
         rads_config.set('Default', 'caller', '')
         rads_config.add_section('System')
         rads_config.set('System', 'gpu_id', "-1")  # Always running on CPU
-        rads_config.set('System', 'input_folder', '/home/ubuntu/resources/data')
-        rads_config.set('System', 'output_folder', '/home/ubuntu/resources/output')
-        rads_config.set('System', 'model_folder', '/home/ubuntu/resources/models')
-        rads_config.set('System', 'pipeline_filename', '/home/ubuntu/resources/models/' + model_name + '/pipeline.json')
+        rads_config.set('System', 'input_folder', '/workspace/resources/data')
+        rads_config.set('System', 'output_folder', '/workspace/resources/output')
+        rads_config.set('System', 'model_folder', '/workspace/resources/models')
+        rads_config.set('System', 'pipeline_filename', '/workspace/resources/models/' + model_name + '/pipeline.json')
         if logic_task == 'reporting':
             rads_config.set('System', 'pipeline_filename',
-                            '/home/ubuntu/resources/reporting/' + parameters['UserConfiguration']['default'])
+                            '/workspace/resources/reporting/' + parameters['UserConfiguration']['default'])
         rads_config.add_section('Runtime')
         rads_config.set('Runtime', 'reconstruction_method',
                         SharedResources.getInstance().user_configuration['Predictions']['reconstruction_method'])