/LA-Net

Primary LanguagePython

LA-Net: Layer Attention Network for 3D-to-2D Retinal Vessel Segmentation in OCTA Images

This work has been published at Physics in Medicine & Biology.

DOI 10.1088/1361-6560/ad2011

The implementation of this paper is based on PyTorch and verified under public OCTA-500 dataset.


|- root
|-- train.py - "The training code for LA-Net."
|-- test.py - "The test code for LA-Net."
|-- train+.py - "The training code for LA-Net+."
|-- test+.py - "The test code for LA-Net+."
  • First, train LA-Net using train.py, and the top 3 results will be saved in the logs/best_model directory.
  • Then, use test.py to test and generate the features of the last layer of LA-Net. Note: the test.py file includes the npy parameter. When npy=1, it means generating features for training LA-Net+. When npy=2, it means generating the segmentation results of LA-Net.
  • Next, train LA-Net+ using train+.py.
  • Finally, test LA-Net+ using test+.py.