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https://github.com/vendenix/brainmetasegmentatorui-back

API for apply AI model on DICOMS files with a dicom web server (orthanc for demo)
https://github.com/vendenix/brainmetasegmentatorui-back

api brain docker-compose flask macosx metastases ohif-viewer python ubuntu unetr-segmentation windows

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API for apply AI model on DICOMS files with a dicom web server (orthanc for demo)

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README 

        
# Welcome to the SegmentationUI Back-end Project



This project aims to enable the OHIF Viewer interface to launch a deep learning algorithm designed to detect brain metastases, based on the UNETR architecture. This part of the project serves as an API to communicate between the Orthanc DICOM Web Server for retrieving/managing medical data in DICOM format and the fine-tuned UNETR deep learning model for brain metastasis segmentation.



## Important



This project is divided into two parts:

- The back-end part (this repository)

- The front-end part is accessible [here](https://github.com/VendenIX/BrainMetaSegmentatorUI-Front).



## Configuration Prerequisites to Run the Model on the Repository



| Resource               | Requirement                                             |

|------------------------|----------------------------------------------------------|

| RAM                    | 8GB VRAM                                                 |

| GPU                    | RTX 3050 Cuda                                            |



## Architecture 



![Architecture de OHIF](images_readme/architecture.drawio.png)



### Front-end



The front-end of the project is responsible for the user interface and interaction. The primary code for the MetIA functionality is located in the `extensions/MetIA/` directory. The structure is as follows:



- `modes/` - Contains the different modes of the application.

- `extensions/MetIA/` - Houses the main code for the MetIA functionalities.

- `routes/` - Manages the routes for the front-end components.



### Back-end



The back-end handles the processing, database management, and integration with external tools. The main back-end components include:



- `api.py` - Contains the main code along with all the routes for the back-end services.

- `segmentation.py` - This code is responsible for launching the model.

- `unetr/` - This directory contains the code for the UNETR model.

- `BDD/mesures.sql/` - Stores the database and SQL scripts for the tracking.

- `Orthanc/` - All code related to Orthanc, a DICOM Web server, is located in this directory.



## A Sequence Diagram of a Segmentation Request



![Diagramme de séquence](images_readme/sequence_diagram.png)



## To Install the Pre-trained Weights (required to run the deep model):



[Download the pre-trained weights](https://drive.google.com/file/d/1kR5QuRAuooYcTNLMnMj80Z9IgSs8jtLO/view)



Place this 300 MB file in **unetr/pretrained_models/**



## Install the Libraries:

/!\ Use a new environnement with python 3.9

```

conda create --name=IRM-Project python=3.9

conda activate IRM-Project

```



```bash

pip install -r requirements.txt

```

If you encounter difficulties installing, try installing the libraries incrementally. Special mention for installing PyTorch:

```bash

pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu111

```



# Launch the Back-end in Development Mode:

## Start the Orthanc DICOM Web Server and nginx proxy:



Create a network named 'pacs' with Docker if it hasn't been created:

```bash

docker network create pacs

```



Then, start the Docker stack by navigating to the root of the repository:

```bash

docker-compose -f Orthanc/docker-compose.yml up -d

```



## Start the Flask API:

To start the API:

```bash

python3 api.py

```



# Launch the Back-end in Production Mode:

## For Windows:

Ensure that the front-end and back-end repositories are in the same directory:

```bash

git checkout deploiementWindows

```



Specify the path of the UNETR model weights in the `.env` file.

Example:

```env

MODEL_PATH='./models/checkpoint_epoch1599_val_loss0255.cpkt'

```



Execute the `start_services.bat` file:

```bash

# You can create a shortcut on your desktop or make it executable

```



Install your DICOM Web Server Orthanc here : https://www.orthanc-server.com/download-windows.php 



Adapt the path of the environnemnt in the start_services.bat file line 33 (only the path of the activate.bat file and the name of the environnment)



## If You Want to Quickly Delete Medical Data from Your Local Orthanc Server

Clean-up:

```bash

rm -rf ./Orthanc/orthanc-db/*

```



Alternatively, you can delete everything cleanly using the administration interface available at localhost:8042, or via OHIF on the front-end.



## If a Password Is Required, Use the Following:

Login: mapdr

Password: changestrongpassword



## To Shut Down the DICOM Web Server, Use the Following Command:

```bash

docker-compose -f Orthanc/docker-compose.yml down

```



And optionally, if you want to remove the pacs network:

```bash

docker network rm pacs # if you want to remove the network

```