• VesselNet
  • About Model
  • Mertic
  • Project Structure
    • First to run
    • Pretrained Model
    • Test your own image
  • Reference
  • Future Work

A DenseBlock-Unet for Retinal Blood Vessel Segmentation

Notice：This Project structure updated on 9th June！

You can find old version in branch old

This model is inspired by DenseNet and @orobix/retina-unet, I modify the Conv2d block to DenseBlock and finally I get better result. The DenseBlock struct is shown below. This struct maximisely use the extracted feature. If u want further information, please read the DenseNet Paper and code

Tried With 40 images of DRIVE dataset and DenseBlock-Unet model. Results on DRIVE database:

The structure is based on my own DL_Segmention_Template. Difference between this project and the template is that we have metric module in dir: perception/metric/. To get more Information about the structure please see readme in DL_Segmention_Template.

You can find model parameter in configs/segmention_config.json.

please run main_trainer.py first time, then you will get data_route in experiment dir. Put your data in there, now you can run main_trainer.py again to train a model.

The model is trained with DRIVE dataset on my own desktop (intel i7-7700hq,24g,gtx1050 2g) within 30 minutes. Dataset and pretrained model can be found here. For Chinese, you can download here.

If u want to test your own image, put ur image to (VesselNet)/test/origin, and change the img_type of predict settings in configs/segmention_config.json, run main_test.py to get your result. The result is in (VesselNet)/test/result

This project is based on the following 2 papers:

U-Net: Convolutional Networks for Biomedical Image Segmentation

Densely Connected Convolutional Networks

First of all, I choose 48x48pix patches to train the model. The patch size means that model can't be too deep. So in future, I want to test 128X128pix patches and 96x96 patches.

Second, Attention-based Unet and DeepLab-v3+ are also worth to try.