oct-stargardtretina-seg

Code for the paper "Retinal Boundary Segmentation in Stargardt Disease Optical Coherence Tomography Images Using Automated Deep Learning"

Link: https://tvst.arvojournals.org/article.aspx?articleid=2770902

If the code and methods here are useful to you and aided in your research, please consider citing the paper.

Dependencies

• Python 3.6.4
• Keras 2.2.4
• tensorflow-gpu 1.8.0
• h5py
• Matplotlib
• numpy

Training a model (patch-based)

1. Modify load_training_data and load_validation_data functions in train_script_patchbased_general.py to load your training and validation data (see comments in code).
2. Choose one of the following and pass as first training parameter as shown in code:
   • model_cifar (Cifar CNN)
   • model_complex (Complex CNN)
   • model_rnn (RNN) [default]
3. Can change the desired patch size (PATCH_SIZE) as well as the name of your dataset (DATASET_NAME).
4. Run train_script_patchbased_general.py
5. Training results will be saved in the location defined by parameters.RESULTS_LOCATION. Each new training run will be saved in a new seperate folder named with the format: . Each folder will contain the following files:
   • config.hdf5 (summary of parameters used for training)
   • stats_epoch#.hdf5 (training and validation results for each epoch up to epoch #)
   • one or more model_epoch&.hdf5 files containing the saved model at each epoch &

Training a model (semantic)

1. Modify load_training_data and load_validation_data functions in train_script_semantic_general.py to load your training and validation data (see comments in code).
2. Choose one of the following and pass as first training parameter as shown in code:
   • model_residual_scSE (Residual U-Net with scSE blocks) [default]
   • model_residual_5deep (Residual U-Net with 5 pooling layers)
3. Choose to use augmentations [default] or no augmentations (see relevant parameters in code) as utilised in the paper.
4. Can change the name of your dataset (DATASET_NAME).
5. Run train_script_semantic_general.py
6. Training results will be saved in the location defined by parameters.RESULTS_LOCATION. Each new training run will be saved in a new seperate folder named with the format: <DATASET_NAME>. Each folder will contain the following files:
   • config.hdf5 (summary of parameters used for training)
   • stats_epoch#.hdf5 (training and validation results for each epoch up to epoch #)
   • one or more model_epoch&.hdf5 files containing the saved model at each epoch &

Evaluating a model (patch-based)

1. Modify load_testing_data function in eval_script_patchbased_general.py to load your testing data (see comments in code).
2. Specify trained network folder to evaluate.
3. Specify filename of model to evaluate within the chosen folder: model_epoch&.hdf5
4. Run eval_script_patchbased_general.py
5. Evaluation results will be saved in a new folder (with the name no_aug_<DATASET_NAME>.hdf5) within the specified trained network folder. Within this, a folder is created for each evaluated image containing a range of .png images illustrating the results qualitatively as well as an evaluations.hdf5 file with all quantitative results. A new config.hdf5 file is created in the new folder as well as results.hdf5 and results.csv files summarising the overall results after all images have been evaluated.

Evaluating a model (semantic)

1. Modify load_testing_data function in eval_script_semantic_general.py to load your testing data (see comments in code).
2. Specify trained network folder to evaluate.
3. Specify filename of model to evaluate within the chosen folder: model_epoch&.hdf5
4. Run eval_script_semantic_general.py
5. Evaluation results will be saved in a new folder (with the name no_aug_<DATASET_NAME>.hdf5) within the specified trained network folder. Within this, a folder is created for each evaluated image containing a range of .png images illustrating the results qualitatively as well as an evaluations.hdf5 file with all quantitative results. A new config.hdf5 file is created in the new folder as well as results.hdf5 and results.csv files summarising the overall results after all images have been evaluated.

Still to be added

• Instructions / code for ensembling
• Instructions / code for preprocessing data with contrast enhancement (Girard filter)