Table of Contents

• General info
• Methods pipeline
• Contents
• Getting started

General Info

Welcome to the official repository for NDNC-Net, a deep-learning approach designed to correct rotational distortions in Optical Coherence Tomography (OCT) images. NDNC-Net integrates the rotation dynamics model of the Rotatable Diametrically Magnetized Cylinder Permanent Magnet (RDPM) and is trained using synthetic data.

The current version includes the following models:

1. Nonuniform Rotational Distortion Detection Network (NDNet)

2. Nonuniform Rotational Distortion Correction Network (NCNet)

Methods Pipeline

The methods for generating the dataset for the NDNC-Net model, the specific working principles, and the evaluation of the model's image restoration capabilities are comprehensively detailed in the accompanying paper. The diagram below illustrates the dataset generation method and the inverse resampling process used for NURD correction.

Contents

Resample

This folder includes the Python scripts that can generate the synthetic dataset with accurate pairs of resampling distance variation vector (RDVV) and distorted images for further NDNC-Net training.

NDNet

This folder includes the Python scripts that can train the NDNet model.

NCNet

ncnet.py is the Python script for inference of the NCNet model.

OCT Restore

oct_restore.py is the Python script that can correct distorted images using the trained NDNet and NCNet models.

Getting Started

Setup

Python dependencies:

• pytorch
• opencv
• sklearn
• skimage

We provide a requirements.txt including all of the above dependencies. To create a new conda environment and install the dependencies, run:

conda create --name ndnc-net python=3.9
conda activate ndnc-net
pip install -r requirements.txt

Initialize

Obtain the NCNet checkpoint from Google Drive, and create a new directory named weights and place the checkpoint within.

Prepare the Synthetic Dataset

The dataset can be downloaded from Google Drive. You need to rename the folder to images(by default) and place it in the root directory.

To create the dynamics curves, run the following command:

python create_random.py --num_datasets 100 # replace 100 with the number of curves you want to generate

To create distorted images, run the following command:

python resample.py --num_samples 100 # replace 100 with the number of samples you want to generate

Train NDNet

We provide a pre-trained NDNet model best.pt which can be found in the src/yolo/train/weights directory.

To train your own NDNet, prepare the dataset and dataset.yaml file, and run:

python train.py

After training, the best model will be saved in the weights directory.

If you want to evaluate the model, run:

python detect.py

Results will be saved in the ./runs/detect directory.

Inverse Resampling

Run

python oct_restore.py

to start inference. Results will be saved in the ./outputs.

Demonstration

A synthetic dataset for testing is available for download on Google Drive, which includes original distortion-free samples and the corresponding synthetic distorted images. This dataset can be used to compare the OCT images after correction with the original distortion-free images.