ShawnBIT / AI-Challenger-Retinal-Edema-Segmentation

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

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. If you need the dataset for scientific research purposes, please send a message to me via Zhihu.

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.

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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.

Original Image	Ground Truth	UNet（Dice+WCE）	UNet++（ELDice+WCE）	Dialted UNet++	UNet&UNet++ Fusion

1. Getting Started

Clone the repo:

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:

pip install -r requirements.txt

2. Data Prepare

Data Download

First, you are supposed to make a dataset directory.

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.

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.

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:

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:

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:

CUDA_VISIBLE_DEVICES=2 python3 eval.py -d ./data/dataset/ -l ./data/data_path -j 32 --vis --fusion 

To test a single model:

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:

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
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
Dialted UNet++	Yes	5.32M	WCE+ELDice+BCE	0.807(+3.5%)	0.978(+0.6%)			link
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

• Add Presentation PPT
• Add Dataset source
• Add Data prepare
• Add Visualization demo
• Add Usage
• Add pretrained model
• Add Results
• Add Future work
• Add Reference
• Add Experience summary

8. Acknowledgement

• GPU support of DeepWise
• Mentor Prof. Yizhou Wang's guidence
• The host,AI challenger platform

9. Reference

Paper

• UNet++: A Nested U-Net Architecture for Medical Image Segmentation (2018 MICCAI)
• 3D Segmentation with Exponential Logarithmic Loss for Highly Unbalanced Object Sizes (2018 MICCAI oral)
• D-LinkNet: LinkNet with Pretrained Encoder and Dilated Convolution for High Resolution Satellite Imagery Road Extraction (2018 CVPR workshop)
• U-Net: Convolutional Networks for Biomedical Image Segmentation (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.）