基于gan的眼睛视网膜血管识别切分内含数据集.zip

# V-GAN # ### Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks [[original paper](https://arxiv.org/pdf/1706.09318.pdf)] [[cite](https://scholar.google.co.kr/scholar?hl=ko&as_sdt=0%2C5&q=retinal+vessel+segmentation+adversarial&btnG=#d=gs_cit&p=&u=%2Fscholar%3Fq%3Dinfo%3Ao5KHdMCl4fUJ%3Ascholar.google.com%2F%26output%3Dcite%26scirp%3D3%26hl%3Dko)]###  ## Package Dependency ## scikit_image==0.12.3 numpy==1.12.0 matplotlib==2.0.0 scipy==0.18.1 Keras==2.0.4 Pillow==4.1.1 skimage==0.0 scikit_learn==0.18.1 ## Directory Hierarchy ## ``` . ├── codes │   ├── evaluation.py │   ├── inference.py │   ├── model.py │   ├── train.py │   ├── utils.py ├── data │   ├── DRIVE │   └── STARE ├── evaluation │   ├── DRIVE │   └── STARE ├── inference_outputs │   ├── DRIVE │   └── STARE ├── pretrained │   ├── DRIVE_best.h5 │   ├── DRIVE_best.json │   ├── STARE_best.h5 │   ├── STARE_best.json │   ├── auc_pr_STARE.npy │   ├── auc_roc_DRIVE.npy │   ├── auc_roc_STARE.npy │   └── auc_roc_pr_DRIVE.npy └── results ├── DRIVE └── STARE ``` **codes** : source codes **data** : original data. File hierarchy is modified for convenience. **evaluation** : quantitative and qualitative evaluation. **inferenced_outputs** : outputs of inference with our model **pretrained** : pretrained model and weights **results** : results of other methods. These image files are retrieved from [here](http://www.vision.ee.ethz.ch/~cvlsegmentation/driu/downloads.html) ## Training ## Move to **codes** folder and run train.py ``` python train.py --ratio_gan2seg=<int> --gpu_index=<int> --batch_size=<int> --dataset=[DRIVE|STARE] --discriminator=[pixel|patch1|patch2|image]``` ### arguments ### ratio_gan2seg : trade-coefficient between GAN loss and segmentation loss gpu_index : starting index for gpus to be used batch_size : number of images per a batch dataset : type of a dataset (DRIVE or STARE) discriminator : type of a discriminator (pixel or patch1 or patch2 or image) **CAVEAT** Training with the current codes requires main memory more than 50 GB and GPUs dedicated to Deep Learning. If no such system is available, it is recommended to use pre-trained model only for inference. ## Inference ## Move to **codes** folder and run inferency.py ``` python inference.py``` Outputs of inference are generated in **inference_outputs** folder. ## Evaluation ## Move to **codes** folder and run evaluation.py ``` python evaluation.py``` Results are generated in **evaluation** folder. Hierarchy of the folder is ``` . ├── DRIVE │   ├── comparison │   ├── measures │   └── vessels └── STARE ├── comparison ├── measures └── vessels ``` **comparison** : difference maps of our method **measures** : ROC and PR curves **vessels** : vessels superimposed on segmented masks