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| <h1 class="mb-4 mt-4">Documentation</h1> | |
| <section id="PARROT"> | |
| <h2>PARROT: Platform for ARtificial intelligence guided Radiation Oncology Treatment</h2> | |
| <p> | |
| <strong>PARROT</strong> is a local web application. It provides different modules that allow | |
| users to: | |
| </p> | |
| <ul> | |
| <li> | |
| <p>Run AI models.</p> | |
| </li> | |
| <li> | |
| <p>Visualize CT and (soon) MR images.</p> | |
| </li> | |
| <li> | |
| <p>Visualize, predict or modify segmentation information.</p> | |
| </li> | |
| <li> | |
| <p>Visualize, predict, evaluate, and compare dose distributions.</p> | |
| </li> | |
| </ul> | |
| </section> | |
| <section id="installation"> | |
| <h2>Installation steps</h2> | |
| <h3>PARROT</h3> | |
| <p>There are two ways to install PARROT, downloading the executable file of the last release from | |
| the <a href="index.html">Home</a> page is recommended. Otherwise, the code with building | |
| instructions are also available on our <a href="https://gitlab.com/ai4miro/parrot">Gitlab | |
| repository</a>.</p> | |
| <h3>Orthanc</h3> | |
| <p> | |
| Patient data are stored using a Orthanc daemon, a lightweight, standalone DICOM server, which | |
| ensures the confidentiality of patient files. To install Orthanc, either click on the link on | |
| PARROT's home tab (see Figure 1) or click on the following link: <a | |
| href="https://orthanc.uclouvain.be/downloads/windows-64/installers/OrthancInstaller-Win64-24.2.3.exe">Install | |
| Orthanc</a>. | |
| </p> | |
| <figure> | |
| <img src="img/install_orthanc.png" alt="Install orthanc" class="img-fluid mb-3"> | |
| <figcaption>Figure 1: Home screen, link to install Orthanc.</figcaption> | |
| </figure> | |
| <p class="warning"> | |
| ⚠ The Orthanc server will run on <a target="_blank" href="http://localhost:8042/">port 8042</a> | |
| while nginx server for the application will run on <a target="_blank" | |
| href="http://localhost:2000/">port 2000</a>. | |
| </p> | |
| </section> | |
| <section id="organisation"> | |
| <h2>Organisation of the application</h2> | |
| The application is divided in multiple tabs that users can visit through the top navigation bar. | |
| This section provides a description of each tabs and their features. | |
| <section id="patient-management"> | |
| <h3>Patient Management</h3> | |
| <p>Users can import patient DICOM data (files or zip) from this screen to a local <a | |
| href="https://www.orthanc-server.com/">Orthanc</a> daemon. A list of patient data | |
| present in the Orthanc server is displayed with summarized information. Users can load the | |
| patient data to PARROT by clicking on the Load button. Users can delete a patient from the | |
| server with the red arrow on the right.</p> | |
| <figure> | |
| <img src="img/screen_patient_management.PNG" alt="Patient Management Image" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 2: Patient Management screen.</figcaption> | |
| </figure> | |
| </section> | |
| <section id="study-management"> | |
| <h3>Study Management</h3> | |
| <p>A list of studies of the loaded patient present in the Orthanc server is displayed with | |
| summarized information. Users can load the data into the app by clicking on the Load button. | |
| The left icon displays more complete information from DICOM tags and users can delete a | |
| patient study from the server with the red arrow on the right.</p> | |
| <figure> | |
| <img src="img/screen_study_management.png" alt="Study Management Image" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 3: Study Management screen.</figcaption> | |
| </figure> | |
| </section> | |
| <section id="ai-marketplace"> | |
| <h3>AI Marketplace</h3> | |
| <figure> | |
| <img src="img/screen_ai_marketplace.png" alt="AI Marketplace Image" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 4: AI Marketplace screen.</figcaption> | |
| </figure> | |
| <p>Eight segmentation models and six dose prediction models are made available on HuggingFace and can be downloaded from the AI Marketplace screen (Figure 4). | |
| These models are trained from state-of-the-art architectures such as | |
| nnUNet <a href="#ref1">[1]</a>, SwinUNETR <a href="#ref2">[2]</a> and HDUNet <a href="#ref3">[3]</a> (<a href="ai_models.html">AI models</a>). | |
| We distinguish two categories of models: the segmentation models and the dose prediction | |
| models. A key difference between the two categories is that segmentation models require only | |
| the anatomy of the patient (CT scan or MRI image) whereas dose prediction models also | |
| require binary masks of tumor volumes (TV) and organs at risk (OARs). For that latter, we | |
| implemented a dialog that allows users to map structures present in the current patient data | |
| and the input required for the AI dose prediction model (Figure 7).</p> | |
| <p>To be able to use these models, we also provide a Dockerfile that is automatically used to generate a Docker container to then run the models.</p> | |
| </section> | |
| <section id="ai-models-management"> | |
| <h3>Run AI Models</h3> | |
| <figure> | |
| <img src="img/screen_ai_models_management.png" alt="AI Models Management Image" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 5: AI Models Management screen.</figcaption> | |
| </figure> | |
| <p>To run a model prediction, the user simply clicks on the yellow Prediction button on the | |
| right hand side of the model line. This will open a Python scripting editor to run the code | |
| (Figure 6). Two choices are available to the user: either run the code locally or in one of | |
| the Python environments provided. Users also have the flexibility to run their own | |
| models by loading their own code in the Python scripting editor. | |
| </p> | |
| <div class="row"> | |
| <figure class="col-5"> | |
| <img src="img/python_code_editor.png" alt="Code editor" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 6: Embedded python code editor.</figcaption> | |
| </figure> | |
| <figure class="col-6"> | |
| <img src="img/contour_matching.png" alt="Input matching" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 7: Input matching.</figcaption> | |
| </figure> | |
| </div> | |
| </section> | |
| <section id="patient-modeling"> | |
| <h3>Patient Modeling</h3> | |
| <figure> | |
| <img src="img/screen_patient_modeling.png" alt="Patient Modeling Image" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 8: Patient Modeling screen.</figcaption> | |
| </figure> | |
| <p>Once the prediction is done, users can upload the result to the study to visualize the output in | |
| the Patient Modeling screen (Figure 8). | |
| </p> | |
| </section> | |
| <section id="plan-evaluation"> | |
| <h3>Plan Evaluation</h3> | |
| <figure> | |
| <img src="img/screen_plan_evaluation.png" alt="Plan Evaluation Image" | |
| class="img-fluid mb-3"> | |
| <figcaption>Figure 9: Plan Evaluation screen.</figcaption> | |
| </figure> | |
| <p>A comprehensive display of two dose distributions comparison is presented on the last screen (see Figure 9). | |
| The two dose distributions can originate from the importation step or result from the | |
| inference of ai models. We provide a set of tools for comparison, such as DVH curves, dose | |
| statistics, and the fulfillment of clinical goals. Clinical goals can be uploaded following | |
| an Excel template so that it fits any clinical requirements of the users institutions. The | |
| platform incorporates treatment indications based on NTCP protocols. It has been shown in | |
| multiple studies that predicted dose distributions yield meaningful output when used with | |
| NTCP models for treatment selection decision support <a href="#ref4">[4]</a><a href="#ref5">[5]</a>. This is a useful decision | |
| support tool for the clinical community. | |
| </p> | |
| </section> | |
| <section> | |
| <h3>References</h3> | |
| <p id="ref1">[1] Isensee, F., Jaeger, P. F., Kohl, S. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: a self-configuring | |
| method for deep learning-based biomedical image segmentation. Nature methods, 18(2), 203-211.</p> | |
| <p id="ref2">[2] Hatamizadeh, A., Nath, V., Tang, Y., Yang, D., Roth, H. and Xu, D., 2022. Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images. arXiv preprint arXiv:2201.01266.</p> | |
| <p id="ref3">[3] Nguyen D, Jia X, Sher D, Lin MH, Iqbal Z, Liu H, Jiang S. 3D radiotherapy dose prediction on head and neck cancer patients with a hierarchically densely connected U-net deep learning architecture. Phys Med Biol. 2019 Mar 18;64(6):065020. doi: 10.1088/1361-6560/ab039b. PMID: 30703760.</p> | |
| <p id="ref4">[4] Huet-Dastarac M, Michiels S, Rivas ST, et al. Patient selection for proton therapy using Normal Tissue Complication Probability with deep learning dose prediction for oropharyngeal cancer. Med Phys. 2023; 50: 6201–6214. https://doi.org/10.1002/mp.16431</p> | |
| <p id="ref5">[5] Draguet C., Barragán-Montero A., Chocan Vera M., Thomas M., Populaire P., Defraene G., Haustermans K., Lee J., Sterpin E., | |
| Automated clinical decision support system with deep learning dose prediction and NTCP models to evaluate treatment complications in patients with esophageal cancer, Radiotherapy and Oncology, Volume 176, 2022, https://doi.org/10.1016/j.radonc.2022.08.031.</p> | |
| </section> | |
| <section id="stopping-the-application"> | |
| <h3>Stopping the application</h3> | |
| <ul> | |
| <li> | |
| <p> | |
| The nginx server will continue running, even when you close the web browser window. | |
| To completely stop the server, double click the <strong>'Stop_PARROT.bat'</strong> | |
| file in the <strong>'parrot'</strong> folder (or stop the nginx processes in the | |
| Task Manager). | |
| </p> | |
| </li> | |
| <li> | |
| <p> | |
| The Orthanc server runs as a Windows service and has to be stopped in the | |
| <strong>'Task Manager'</strong> (in the tab <strong>'Services'</strong>). Don't | |
| forget to start the service again when you want to use the <strong>'PARROT | |
| application'</strong>. | |
| </p> | |
| </li> | |
| </ul> | |
| </section> | |
| </section> | |
| <section id="user-guide"> | |
| <h2>User guide</h2> | |
| <section id="user-guide"> | |
| <h3>Load patient</h3> | |
| <iframe width="560" height="315" | |
| src="https://www.youtube.com/embed/iXPMgLM7UYs?si=mNdDxmjIKIThRAfA" | |
| title="YouTube video player" frameborder="0" | |
| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" | |
| referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> | |
| </section> | |
| <section id="edit-contours"> | |
| <h3>Edit contours</h3> | |
| <iframe width="560" height="315" | |
| src="https://www.youtube.com/embed/1YO3PYhbvzs?si=swi0iNqSAI0hjTSU" | |
| title="YouTube video player" frameborder="0" | |
| allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" | |
| referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> | |
| </section> | |
| </section> | |
| </div> | |
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| <nav id="right-menu" class="navbar navbar-light bg-light"> | |
| <nav class="nav flex-column"> | |
| <a class="nav-link" href="#installation">Installation</a> | |
| <a class="nav-link" href="#organisation">Organisation of the application</a> | |
| <ul> | |
| <li><a class="nav-link" href="#patient-management">Patient Management</a></li> | |
| <li><a class="nav-link" href="#study-management">Study Management</a></li> | |
| <li><a class="nav-link" href="#ai-models-management">AI Models Management</a></li> | |
| <li><a class="nav-link" href="#patient-modeling">Patient Modeling</a></li> | |
| <li><a class="nav-link" href="#plan-evaluation">Plan Evaluation</a></li> | |
| </ul> | |
| <a class="nav-link" href="#user-guide">User guide</a> | |
| <ul> | |
| <li><a class="nav-link" href="#load-patient">Load patient</a></li> | |
| <li><a class="nav-link" href="#edit-contours">Edit contours</a></li> | |
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