A modified Attention Unet model with post-process for retina optic disc segmention.

The performance of our model on Messidor-dataset：

I use a modified Attention Unet which input of model is 128x128pix image patches.To know more about attn-unet,please see the paper.When sampling the patches,I focus the algorithm get samples around optic disc.The patches is like that:

so the groundtruth is :

the model is trained on DRION dataset.90 images to train. 19 images to test.

To get the groundtruth of DRION, I write a convert tool,you can find in DRION-DB-PythonTools.

pretrained can be downloaded here.extract them to dir Dataset.

when directly use unet model, we often get some error predictions.So I use a post-process algorithm:

1. predicted area can't be to small.
2. minimum bounding rectangle's height/width or width/height should be in 0.45~2.5

lefted area is the final output.The problem of this algorithm is that the parameters not self-adjusting.so you have to change them if input image is larger or smaller than before.

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 DRION dataset on my own desktop(intel i7-7700hq,24g,gtx1050 2g) within 30 minutes. Datatset

if u want to test your own image,put ur image to (OpticDisc)/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 (OpticDisc)/test/result