AI-Challenger-Retinal-Edema-Segmentation - GitLite

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AI-Challenger-Retinal-Edema-Segmentation

(★ 109)

DeepSeg : 4th place(top3%) solution for Retinal Edema Segmentation Challenge in 2018 AI challenger

파일 탐색기

최종 버전 다운로드 (.zip)

- - info.json
  - info_test.json
  - test.json
  - test_images.txt
  - train.json
  - train_images.txt
  - train_labels.txt
  - val_images.txt
  - val_labels.txt
- - compute_mean_std.py
  - gen_image.py
  - gen_txt.py
- __init__.py
- Seg_dataset.py
- seg_transforms.py
- - 0010.gif
  - 0014.gif
  - label_color.png
- final-pre.pdf
- - __init__.py
  - UNet.py
- - __init__.py
  - init_weights.py
  - layers.py
- __init__.py
- net_builder.py
- __init__.py
- logger.py
- loss.py
- utils.py
- vis.py
_config.yml
eval.py
main.sh
README.md
requirements.txt
test.py
train.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/ShawnBIT/AI-Challenger-Retinal-Edema-Segmentation

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd AI-Challenger-Retinal-Edema-Segmentation

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

pip install -r requirements.txt

requirements.txt 등에 명시된 파이썬 라이브러리를 설치합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

이 레포의 README에 적힌 실제 명령어를 그대로 가져왔습니다.

// repository documentation

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# AI-Challenger-Retinal-Edema-Segmentation
> Our team ranked fourth in objective results and ranked first in subjective results. Finally, we got the fourth place in this challenge. And our final presentation PPT is [as follows](https://github.com/ShawnBIT/AI-Challenger-Retinal-Edema-Segmentation/blob/master/materials/final-pre.pdf). If you need the dataset for scientific research purposes, please send a message to me via [Zhihu](https://www.zhihu.com/people/hellowangshushu).

## 0. Introduction

We build an end-to-end **multi-task framework** that can simultaneously detect and segment retinal edema lesions. We use the latest **UNet++** model to better integrate high-level and low-level features for segmentation and add a classification head at the highest level feature map for detection. For two types of  small lesions, we use a novel **exponential logarithmic loss** function to enhance the segmentation performance. Meanwhile, we introduce the **dilated convolution module**, which significantly increases the receptive field of the model and improves the segmentation performance of big lesions. More importantly, only random horizontal flip data augmentation is needed and no need for post-processing.

Finally, the dice of single model on the test set is **0.736**. The dice of fusion model on the test set is **0.744** and the detection AUC is **0.986**. The memory of inference stage is **7.3G(TITAN Xp)** when we set batch is 8 and the inference time is **9.5s** per patient.

****
The visualization of the predictions of our models in the validation set (case 11 and case 15) is as follows. It is obvious that although we use a 2D model, our model holds **good continuity** in 3D dimension. The gif images are powered by [gif5.net](http://www.gif5.net/).

|Original Image|Ground Truth| UNet（Dice+WCE）| UNet++（ELDice+WCE）| Dialted UNet++|UNet&UNet++ Fusion| 
|---|---|---|---|---|---
<p align="center">
  <img src="https://github.com/ShawnBIT/AI-Challenger-Retinal-Edema-Segmentation/blob/master/materials/figures/0010.gif" width="1000"/>  
  <img src="https://github.com/ShawnBIT/AI-Challenger-Retinal-Edema-Segmentation/blob/master/materials/figures/0014.gif" width="1000"/>
  <img src="https://github.com/ShawnBIT/AI-Challenger-Retinal-Edema-Segmentation/blob/master/materials/figures/label_color.png" width="250"/>
</p>

## 1. Getting Started

Clone the repo:

```bash
  git clone https://github.com/ShawnBIT/AI-challenger-Retinal-Edema-Segmentation.git
  ```

#### Requirements
 ```
python>=3.6
torch>=0.4.0
torchvision
argparse
numpy
pillow
scipy
scikit-image
sklearn
 ```
 Install all dependent libraries:
  ```bash
  pip install -r requirements.txt
  ```
 
## 2. Data Prepare

#### Data Download
First, you are supposed to make a dataset directory.
```bash
cd data
mkdir dataset
```
 Then you have to put the three zip files in the directory 'data/dataset' and unzip them in the current directory.
```bash
unzip ai_challenger_fl2018_testset.zip
unzip ai_challenger_fl2018_trainingset.zip
unzip ai_challenger_fl2018_validationset.zip
```

#### Data Pre-process
Because we stack the bottom and upper slice to form a three channel image to model the content between slices, we have to pre-process the original images. 
```bash
cd ..
python utils/gen_image.py
```
#### Data Structure

```
  data/dataset
  ├── Edema_trainingset
  |   ├── original_images
  │   ├── label_images
  │   ├── trans3channel_images
  ├── Edema_validationset
  |   ├── original_images
  │   ├── label_images
  │   ├── trans3channel_images
  │   ├── groundtruth
  ├── Edema_testset
  |   ├── original_images
  │   ├── trans3channel_images
  ```

## 3. Usage
To train the model:
```bash
CUDA_VISIBLE_DEVICES=4,5,6,7 python3 train.py --name unet_nested -d ./data/dataset/ -l ./data/data_path --batch-size 16 -j 16 --epochs 100 -o Adam --lr 0.001 --lr-mode poly --momentum 0.9 --loss mix_33
  
CUDA_VISIBLE_DEVICES=4,5,6,7 python3 train.py --name unet  -d ./data/dataset/ -l ./data/data_path --batch-size 32 -j 32 --epochs 100 -o Adam --lr 0.001 --step 20 --momentum 0.9 --loss mix_3
  ```
To evaluate a single model:
```bash
CUDA_VISIBLE_DEVICES=2 python3 eval.py -d ./data/dataset/ -l ./data/data_path -j 32 --vis --seg-name unet_nested --seg-path result/ori_3D/train/unet_nested/checkpoint/model_best.pth.tar
  ```
To evaluate the fusion model:
```bash
CUDA_VISIBLE_DEVICES=2 python3 eval.py -d ./data/dataset/ -l ./data/data_path -j 32 --vis --fusion 
  ```
To test a single model:
```bash
CUDA_VISIBLE_DEVICES=2 python3 test.py -d ./data/dataset/ -l ./data/data_path -j 32 --seg --det --seg-name unet_nested --seg-path result/ori_3D/train/unet_nested/checkpoint/model_best.pth.tar
  ```
To test the fusion model:
```bash
CUDA_VISIBLE_DEVICES=2 python3 test.py -d ./data/dataset/ -l ./data/data_path -j 32 --seg --det --fusion
  ```
  
## 4. Results

| Model |Multi-Task| Params| Loss| Val_Dice| Val_Auc| Test_Dice| Test_Auc|Checkpoint|
|---|---|---|---|---|---|---|---|---
| ResNet18(*pre)  |No| 11.18M | BCE| -    | 0.971| -| 0.904| - |
| UNet            |No| 2.47M  | WCE+Dice| 0.772| -| 0.683| -|-|
| UNet            |Yes| 2.47M | WCE+Dice+BCE| 0.785(+1.3%)|0.985(+1.4%) |0.701(+1.8%) |- |[link](https://pan.baidu.com/s/1pa8NC09nZnLq3Cs37TduXg)|
| UNet++          |Yes| 2.95M | WCE+Dice+BCE| 0.784(+1.2%)|0.986(+1/5%)| -| -|-|
| UNet++          |Yes| 2.95M | WCE+ELDice+BCE|0.799(+2.7%)|0.989(+1.8%)|0.736(+5.3%) | -|[link](https://pan.baidu.com/s/1pa8NC09nZnLq3Cs37TduXg)|
| Dialted UNet++  |Yes| 5.32M | WCE+ELDice+BCE|**0.807(+3.5%)**|0.978(+0.6%) | | |[link](https://pan.baidu.com/s/1pa8NC09nZnLq3Cs37TduXg)|
| Fusion(*)      |-|- | -|0.805(+3.3%)|**0.991(2%)**|**0.744(6.1%)**|**0.986(+8.2%)**|-|

## 5. Experience Summary

## 6. Future Work
  * 3D Segmentation Model (patch-wise segmention)
  * Mask R-CNN Detection Model (segmention based on detection)
  * More Data Augmentation (Train and Test)
  * Content Encoding Module
  * scSE Attention Module

## 7. To do
- [x] Add Presentation PPT
- [x] Add Dataset source
- [x] Add Data prepare
- [x] Add Visualization demo
- [x] Add Usage
- [x] Add pretrained model
- [x] Add Results
- [x] Add Future work
- [x] Add Reference
- [ ] Add Experience summary

## 8. Acknowledgement
 * GPU support of [DeepWise](http://www.deepwise.com/) 
 * Mentor Prof. [Yizhou Wang](http://www.idm.pku.edu.cn/staff/wangyizhou/)'s guidence
 * The host,[AI challenger](https://challenger.ai/) platform

## 9. Reference
#### Paper
 * [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://arxiv.org/pdf/1807.10165.pdf) (2018 MICCAI)
 * [3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes](https://arxiv.org/pdf/1809.00076.pdf) (2018 MICCAI oral)
 * [D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction](http://openaccess.thecvf.com/content_cvpr_2018_workshops/papers/w4/Zhou_D-LinkNet_LinkNet_With_CVPR_2018_paper.pdf) (2018 CVPR workshop)
 * [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/pdf/1505.04597.pdf) (2015 MICCAI)

#### Code
 * https://github.com/fyu/drn (Our framework style mainly refered to this repository.)
 * https://github.com/ozan-oktay/Attention-Gated-Networks (Our model style mainly refered to this repository.）