# image-segmentation

**Repository Path**: zhyantao/image-segmentation

## Basic Information

- **Project Name**: image-segmentation
- **Description**: Mirror of https://github.com/zhyantao/image-segmentation
- **Primary Language**: Python
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: https://github.com/zhyantao/image-segmentation
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2023-12-31
- **Last Updated**: 2025-12-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Invascular OCT Image Segmentation

[![Build Status](https://travis-ci.org/zhyantao/image-segmentation.svg?branch=master)](https://travis-ci.org/zhyantao/image-segmentation)

## Architecture

[Architecture of U-net](./U-net.pdf)

## Dependencies

- CentOS 7
- Python 3.6.5
- [Python-tkinter](https://centos.pkgs.org/7/centos-sclo-rh-x86_64/rh-python36-python-tkinter-3.6.3-3.el7.x86_64.rpm.html)
- Deploy the environment.
  ```shell
  pip install -r requirements.txt -i http://mirrors.aliyun.com/pypi/simple/ --trusted-host mirrors.aliyun.com
  ```

## Usage

### Data Preprocess

- Download source codes, run command:  
  `git clone https://github.com/zhyantao/image-segmentation.git`
- Download [TrainData.tar.gz](https://zhyantao.lanzoui.com/igVZust1bti) to the directory **image-segmentation/**. 
- Change directory:  
  `cd image-segmentation/src/`
- Make original dataset, run command:  
  `sh make_dataset.sh`  
- Data preprocess, run command:  
  `python data_preprocess.py`  
- DIY your dataset, open `DIY_dataset.sh` and modify the number of images you wanna to train and valid, then run command:  
  `sh DIY_dataset.sh`
  
### Training model

```shell
python train.py
```
### Test model

```shell
python test.py
```
### Visualizing the result

```shell
python label_visualization.py
```
## References

1. [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/) 
2. [Deep-Learning Based, Automated Segmentation of Macular Edema in Optical Coherence Tomography](https://www.biorxiv.org/content/biorxiv/early/2017/05/09/135640.full.pdf)
3. [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://arxiv.org/pdf/1511.00561v2.pdf)
4. [Relationship Between a Systemic Inflammatory Marker, Plaque Inflammation, and Plaque Characteristics Determined by Intravascular Optical Coherence Tomography](https://www.ahajournals.org/doi/pdf/10.1161/ATVBAHA.107.145987)
5. [preddy5/segnet](https://github.com/preddy5/segnet)

## Results

For more details, see [nohup.out](https://github.com/toooney/image-segmentation/blob/master/nohup.out)

## Visualizing Learning

```shell
tensorboard --logdir=../logs/
```