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UNet-3-Plus-Pytorch

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Unofficial Pytorch Implementation of UNet3Plus: A Full-Scale Connected UNet for Medical Image Segmentation

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# UNet 3+ Unofficial Pytorch Implementation

This code is Implementation of UNet 3+ in pytorch.

I refered to Tensorflow Implementation of UNet 3+ [github](https://github.com/hamidriasat/UNet-3-Plus).

## UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation [![Hits](https://hits.seeyoufarm.com/api/count/incr/badge.svg?url=https%3A%2F%2Fgithub.com%2Fhamidriasat%2FUNet-3-Plus&count_bg=%2379C83D&title_bg=%23555555&icon=sega.svg&icon_color=%23E7E7E7&title=hits&edge_flat=false)](https://hits.seeyoufarm.com) <a href="/LICENSE"><img src="https://img.shields.io/badge/license-MIT-blue.svg" alt="license" /></a>

[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/unet-3-a-full-scale-connected-unet-for/medical-image-segmentation-on-lits2017)](https://paperswithcode.com/sota/medical-image-segmentation-on-lits2017?p=unet-3-a-full-scale-connected-unet-for)

`Hit star ⭐ if you find my work useful.`

## Table of Contents

-   [UNet 3+](https://arxiv.org/abs/2004.08790) for Image Segmentation in Pytorch.
    -   [Table of Contents](#table-of-contents)
    -   [Installation](#installation)
    -   [Code Structure](#code-structure)
    -   [Config](#config)
    -   [Data Preparation](#data-preparation)
    -   [Models](#models)
    -   [Training](#training)
    -   [Inferencing](#inferencing)
    -   [Acknowledgement](#acknowledgement)

## Installation

**Requirements**

-   Python >= 3.10
-   [Pytorch](https://pytorch.org/get-started/locally/) >= 2.2.0
-   CUDA 12.0

This code base is tested against above-mentioned Python and Pytorch versions. But it's expected to work for latest
versions too.

-   Clone code

```
git clone https://github.com/russel0719/UNet-3-Plus-Pytorch.git UNet3P
cd UNet3P
```

-   Install other requirements.

```
pip install -r requirements.txt
```

## Code Structure

-   **checkpoint**: Model checkpoint and logs directory
-   **configs**: Configuration file
-   **data**: Dataset files (see [Data Preparation](#data-preparation)) for more details
-   **data_preparation**: For LiTS data preparation and data verification
-   **losses**: Implementations of UNet3+ hybrid loss function and dice coefficient
-   **models**: Unet3+ model files
-   **utils**: Generic utility functions
-   **data_generator.py**: Data generator for training, validation and testing
-   **predict.py**: Prediction script used to visualize model output
-   **train.py**: Training script

## Config

Configurations are passed through `yaml` file. For more details on config file read [here](/configs/).

## Data Preparation

-   This code can be used to reproduce UNet3+ paper results
    on [LiTS - Liver Tumor Segmentation Challenge](https://competitions.codalab.org/competitions/15595).
-   You can also use it to train UNet3+ on custom dataset.

For dataset preparation read [here](/data_preparation/README.md).

## Models

This repo contains all three versions of UNet3+.

[//]: # "https://stackoverflow.com/questions/47344571/how-to-draw-checkbox-or-tick-mark-in-github-markdown-table"

-   But we can train `unet3plus_deepsup_cgm` only with OUTPUT.CLASSES = 1 option

Here is a sample code for UNet 3+

```python
INPUT_SHAPE = [1, 320, 320]
OUTPUT_CHANNELS = 1

unet_3P = UNet3Plus(INPUT_SHAPE, OUTPUT_CHANNELS, deep_supervision=False, CGM=False)
unet_3P_deep_sup = UNet3Plus(INPUT_SHAPE, OUTPUT_CHANNELS, deep_supervision=True, CGM=False)
unet_3P_deep_sup_cgm = UNet3Plus(INPUT_SHAPE, OUTPUT_CHANNELS, deep_supervision=True, CGM=True)
```

[Here](/losses/unet_loss.py) you can find UNet3+ hybrid loss.

### Training

To train a model on train dataset call `train.py` with required model type and configurations .

e.g. To train on base model run

```
python train.py MODEL.TYPE=unet3plus
```

### Validation

To validate a model on valid dataset call `validate.py` with required model type and configurations .

e.g. To validate on base model and visualize them, run

```
python validate.py MODEL.TYPE=unet3plus
```

### Inferencing

To inference a model on valid dataset call `predict.py` with required model type and configurations .

e.g. To inference on base model run

```
python predict.py MODEL.TYPE=unet3plus
```

## Acknowledgement

-   [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://arxiv.org/abs/2004.08790)

We appreciate any feedback so reporting problems, and asking questions are welcomed here.

Licensed under [MIT License](LICENSE)

UNet-3-Plus-Pytorch

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