oct-stargardtretina-seg

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

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

Dependencies

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).
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]
Can change the desired patch size (PATCH_SIZE) as well as the name of your dataset (DATASET_NAME).
Run train_script_patchbased_general.py
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 &

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).
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)
Choose to use augmentations [default] or no augmentations (see relevant parameters in code) as utilised in the paper.
Can change the name of your dataset (DATASET_NAME).
Run train_script_semantic_general.py
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 &

Modify load_testing_data function in eval_script_patchbased_general.py to load your testing data (see comments in code).
Specify trained network folder to evaluate.
Specify filename of model to evaluate within the chosen folder: model_epoch&.hdf5
Run eval_script_patchbased_general.py
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.

Modify load_testing_data function in eval_script_semantic_general.py to load your testing data (see comments in code).
Specify trained network folder to evaluate.
Specify filename of model to evaluate within the chosen folder: model_epoch&.hdf5
Run eval_script_semantic_general.py
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.

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