---
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.

<p align="center">
    <img src="https://raw.githubusercontent.com/qchapp/lungs-segmentation-app/refs/heads/master/images/app.png" height="700">
</p>

---

## Try the app
The application is running on [Hugging Face](https://huggingface.co/), try it using this [link](https://huggingface.co/spaces/qchapp/3d-lungs-segmentation)!

#### 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).

---

## 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](https://pytorch.org/get-started/locally/).

After that please run the following command:
```sh
pip install -r requirements.txt
```

---

## Usage
Run:
```sh
python app.py
```
And go to the indicated local URL.

---

## Usage as an API
Install `gradio_client` and run the following Python code:
```py
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())
```

---

## About Lungs Segmentation
If you are interested in the package used for segmentation please check the following [GitHub repository](https://github.com/qchapp/lungs-segmentation)!

---