README.md

metadata

title: Lungs Segmentation Web App
emoji: 🖥️
colorFrom: indigo
colorTo: green
sdk: gradio
sdk_version: 5.49.1
app_file: app.py
pinned: false

🖥️ Lungs segmentation web application

A web-based application for automated lung segmentation using deep learning, powered by Gradio and PyTorch. This tool allows users to upload lung images and obtain segmented outputs efficiently.

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Try the app

The application is running on Hugging Face, try it using this link!

Example File

If you don't have your own .tif image, the app includes a built-in example file that can be used directly from the UI by clicking "Try an example!".

Load from URL (file_url parameter)

You can also provide a .tif file hosted online using a URL parameter.

To do so, simply append ?file_url=... to your app's URL.

Example (hosted on Hugging Face):

https://huggingface.co/spaces/qchapp/3d-lungs-segmentation/?file_url=https://zenodo.org/record/8099852/files/lungs_ct.tif

The application will automatically download the file and load it into the viewer (the operation can take some time).

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Installation

We recommend performing the installation in a clean Python environment.

The code requires python>=3.10, as well as pytorch>=2.0. Please install Pytorch first and separately following the instructions for your platform on pytorch.org.

After that please run the following command:

pip install -r requirements.txt

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Usage

Run:

python app.py

And go to the indicated local URL.

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Usage as an API

Install gradio_client and run the following Python code:

from pathlib import Path
import shutil
from gradio_client import Client, handle_file

client = Client("qchapp/3d-lungs-segmentation")
result_path = client.predict(
    file_obj=handle_file("https://zenodo.org/record/8099852/files/lungs_ct.tif?download=1"),
    api_name="/segment",
)

dest = Path("outputs/mask.tif")
dest.parent.mkdir(parents=True, exist_ok=True)
shutil.copy(result_path, dest)
print("Saved the mask in:", dest.resolve())

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About Lungs Segmentation

If you are interested in the package used for segmentation please check the following GitHub repository!

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