This repo contains the offical PyTroch code for Self-supervised Denoising and Bulk Motion Artifact Removal of 3D Optical Coherence Tomography Angiography of Awake Brain @ MICCAI 2024 paper link
Checkpoint, example data and ROI labels are available at data link.
For preparation, simply put the shared data folder and the checkpoint file under the root of this repo.
To train or test on custom datasets, put the volumes under data/OCTA/[custom volume folders] and (optional) put the indices (split by spaces) of the estimated corrupted B-scans in data/OCTA/[volume folder name]_BMA_index.txt.
Run octa_train.py and octa_test.py for training and testing (inference).
Run octa_test.py --padding to keep the boundary B-scans after inference by padding the input volumes.
Run cnr_msr_normal.py and cnr_msr_corrupted.py to calculate CNR and MSR based on the ROI labels and visualize the scores as well as the ROI bounding boxes.
This repo mainly refers to Magic-VNet for the network architecture and UDVD for the training scripts.
@inproceedings{li2024self,
title={Self-supervised Denoising and Bulk Motion Artifact Removal of 3D Optical Coherence Tomography Angiography of Awake Brain},
author={Li, Zhenghong and Ren, Jiaxiang and Zou, Zhilin and Garigapati, Kalyan and Du, Congwu and Pan, Yingtian and Ling, Haibin},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={601--611},
year={2024},
organization={Springer}
}