oct-choroid-seg

Code for the paper "Automatic choroidal segmentation in OCT images using supervised deep learning methods"

Link: https://www.nature.com/articles/s41598-019-49816-4

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 2.4.3
• tensorflow 2.3.1
• h5py
• Matplotlib
• numpy

BioTeam Github Guidance

Inspect example_data.hdf5

Extract images and segmentation files from example_data.hdf5 with hdf5readimages.py

Run with example_data.hdf5

1. For a quick test, set EPOCHS = 100 in parameters.py.
2. Create results and data directories at the root level directory of this repository.
3. In parameters.py, update the RESULTS_LOCATION and DATA_LOCATION to match the paths of the two newly created directories. Examples:
   • DATA_LOCATION = '/Users/user1/git/ML-Image-Segmentation/data/'
   • RESULTS_LOCATION = '/Users/user1/git/ML-Image-Segmentation/results/'
4. Set BIOTEAM = 0 in parameters.py and save the file.
5. Install a conda environment and all the necessary dependecies by running:

conda env create --name ml_env --file environment.yml

6. Activate the conda environment with conda activate ml_env

7. Run python train_script_semantic_general.py

What to expect

• During training, the dice_coef (Dice Coefficient) increases as tensorflow converges on a better model
• The results directory has one config.hdf5 file and several hdf5 files asigned to an epoch number
• To read any hdf5 file from the results directory, run hdf5scan.py

BioTeam version: Train by reading images from a directory

1. Create a remlmaterials directory at the root level directory of this repository with the following sub-directories: train_images, train_segs, val_images, val_segs, test_images, test_segs.
   • Example data has been added at the root level that you can use for testing in a directory called remlmaterials.
2. Copy the files into the corresponding directories. A minimum of 3 train and val files is required for training
3. In parameters.py, update the INPUT_LOCATION to match the directory created in step one. (Example: INPUT_LOCATION = '/Users/user1/git/ML-Image-Segmentation/remlmaterials/')
4. Set BIOTEAM = 1 in parameters.py and save the file.
5. As before, activate the conda environment with conda activate ml_env.
6. Run python train_script_semantic_general.py

What to expect

1. readdirimages.py will create an hdf5 file img_data.hdf5 with the images and segs files in the same format as the example_data.hdf5
2. img_data.hdf5 can be read with hdf5readimages.py
3. img_data.hdf5 cannot be used as input to the original Kugelman et al code (BIOTEAM = 0) because it contains areas, not boundaries
4. All other files and results are in the same format as before

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). [see example data file and load functions]
2. Choose one of the following and pass as first training parameter as shown in code:
   • model_cifar (Cifar CNN)
   • model_complex (Complex CNN) [default]
   • model_rnn (RNN)
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: (TIMESTAMP) _ (MODEL_NAME) _ (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 &

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). [see example data file and load functions]
2. Choose one of the following and pass as first training parameter as shown in code:
   • model_residual (Residual U-Net)
   • model_standard (Standard U-Net) [default]
   • model_sSE (Standard U-Net with sSE blocks)
   • model_cSE (Standard U-Net with cSE blocks)
   • model_scSE (Standard U-Net with scSE blocks)
3. Can change the name of your dataset (DATASET_NAME).
4. Run train_script_semantic_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: (TIMESTAMP) _ (MODEL_NAME) _ (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). [see example data file and load function]
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. Update MODEL_LOCATION in parameters.py to point to the sub-directory generated during the training within the results folder. (Example: MODEL_LOCATION = '/2021-06-21 17_02_56 U-net exampledata/')
2. Update MODEL_NAME in parameters.py to point to the largest epoch file generated during the training contained within the MODEL_LOCATION sub-directory rreferenced in the previous step. (Example: MODEL_NAME = 'model_epoch100.hdf5')
3. Run eval_script_semantic_general.py
4. 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

• RNN bottleneck and Combined semantic network models
• Code and instructions for preprocessing using contrast enhancement (Girard filter)