RetiFluidNet

RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation

Reza Rasti, Armin Biglari, Mohammad Rezapourian, Ziyun Yang, and Sina Farsiu

Recently accepted by IEEE Transactions on Medical Imaging.

Paper at: https://ieeexplore.ieee.org/document/9980422

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Requirement: Tensorflow 2.4

This code includes six parts:

1. Codes for Reading data (/DataReader)
2. Codes for RetiFluidNet model (/models)
3. Codes for losses (/losses)
4. Codes for Attentions Blocks (/temp)
5. Evaluation Code (/results)
6. Codes for training (/train)

For running the RetiFluidNet on your own system:

If you want to run and compile the RetiFluidNet based on your own network, there are four simple steps:

1. Replace Data paths into train.py
2. Compile and run the train.py

Note: If you want to train the network, after replacing the paths, just run the train.py

To reproduce the result just run the /results.py

Citation

If you find this work useful in your research, please consider citing: “R. Rasti, A. Biglari, M. Rezapourian, Z. Yang and S. Farsiu, "RetiFluidNet: A Self-Adaptive and Multi-Attention Deep Convolutional Network for Retinal OCT Fluid Segmentation," in IEEE Transactions on Medical Imaging, doi: 10.1109/TMI.2022.3228285.”