The goal of this project is to show the implementation of the paper Meshkov, Alexandr, et al. "Deep Learning-Based Segmentation of Post-Mortem Human’s Olfactory Bulb Structures in X-ray Phase-Contrast Tomography." Tomography 8.4 (2022): 1854-1868.
Disclaimer: may be temporary not available.
https://ec2-52-73-12-215.compute-1.amazonaws.com/
Pull this repository and execute:
docker-compose up
open:
Install python (3.8.12 is recomended) neccessary dependencies:
pip install -r http_client/requirements.txt -r sample_segmentation/requirements.txt -r layers_segmentation/requirements.txt
than open 3 console tab and execute in each tab separately
python http_client/run.py
python layers_segmentation/server.py
python sample_segmentation/server.py
open:
The app is developed using the microservice approach. Each server is launched in Docker (blue containers on the image below), which are interconnected in one docker-compose file (red one).
In AWS also Apache 2.4 was utilised as a reverse proxy for port 5000.
We hope that this scheme will be helpful if new features should be implemented. In that case, the corresponding code should be put in the Docker container, and connected to Central Processor (see http_client/app/views.py) afterwards.
- enable CUDA for inference
- add link to published paper and citation column
- run flask WSGI as gunicorn
- redirect internal service error to 400