OCT preprocessing: Fully Convolutional Boundary Regression for Retina OCT Segmentation

Introduction

The code provides image reading from raw Spectralis(.vol) and Cirrus(.img) and Matlab files (.mat). It supports retina flatten, cropping, reconstruction and visualization e.t.c

We provide Matlab preprocessing steps and results in the papers:

Fully Convolutional Boundary Regression for Retina OCT Segmentation (MICCAI2019)

Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT (BOE2019)

The paper's source code is patented and licensed thus will not be publicly available at current stage.

Getting Started

Prepare dataset

Diabetic macular edema dataset (download)

Healthy and Multiple Sclerosis dataset (download)

Flattening retina and save images

Healthy and MS subjects

• Change ./hc/filename.txt and ./hc/segname.txt to contain your downloaded data volumes and delineations

• Run ./Scripts/generate_hc_train('./hc/filename.txt','./hc/segname.txt')

DME

• Change filenames in ./Scripts/generate_dme_train.m to contain your downloaded data and run

Each Bscans will be saved into 2D images (.png) and its manual labels are in Json format (.txt).

# The python code for reading manual labels
# self.labellist = sorted(list(Path('Your label path').glob('*.txt')))
with open(str(self.labellist[idx]),'r') as f:
    dicts = json.loads(f.read())
bds = np.array(dicts['bds'], dtype=np.float) - 1 
mask = np.array(dicts['lesion'])

MICCAI2019 results

• Download the results and unzip it under the folder Evaluation

• Healthy and MS evaluation: Run hc_eval.m

  Training: hc09-hc14 and ms13-ms21 (6 healthy and 9 MS, hc09, ms13, ms14 can be used for validation) Testing: hc01-hc08 and ms01-ms12 (8 healthy and 12 MS)

• DME evaluation: Run dme_eval.m

  Standard 50%-50% split are used as in the literature with a fixed training epoch.

Citation

If you are using the healthy and MS dataset, please cite:

@article{he2019retinal,
  title={Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls},
  author={He, Yufan and Carass, Aaron and Solomon, Sharon D and Saidha, Shiv and Calabresi, Peter A and Prince, Jerry L},
  journal={Data in brief},
  volume={22},
  pages={601--604},
  year={2019},
  publisher={Elsevier}
}

@inproceedings{he2019fully,
  title={Fully Convolutional Boundary Regression for Retina OCT Segmentation},
  author={He, Yufan and Carass, Aaron and Liu, Yihao and Jedynak, Bruno M and Solomon, Sharon D and Saidha, Shiv and Calabresi, Peter A and Prince, Jerry L},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={120--128},
  year={2019},
  organization={Springer}
}

@article{he2020structured,
  title={Structured layer surface segmentation for retina OCT using fully convolutional regression networks},
  author={He, Yufan and Carass, Aaron and Liu, Yihao and Jedynak, Bruno M and Solomon, Sharon D and Saidha, Shiv and Calabresi, Peter A and Prince, Jerry L},
  journal={Medical Image Analysis},
  pages={101856},
  year={2020},
  publisher={Elsevier}
}