Bibliographic Details
| Title: |
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction |
| Authors: |
Li, Shijie, Alexopoulos, Palaiologos, Vellappally, Anse, Zambrano, Ronald, Gadi, Wollstein, Gerig, Guido |
| Publication Year: |
2023 |
| Collection: |
Computer Science |
| Subject Terms: |
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition |
| More Details: |
Strong speckle noise is inherent to optical coherence tomography (OCT) imaging and represents a significant obstacle for accurate quantitative analysis of retinal structures which is key for advances in clinical diagnosis and monitoring of disease. Learning-based self-supervised methods for structure-preserving noise reduction have demonstrated superior performance over traditional methods but face unique challenges in OCT imaging. The high correlation of voxels generated by coherent A-scan beams undermines the efficacy of self-supervised learning methods as it violates the assumption of independent pixel noise. We conduct experiments demonstrating limitations of existing models due to this independence assumption. We then introduce a new end-to-end self-supervised learning framework specifically tailored for OCT image denoising, integrating slice-by-slice training and registration modules into one network. An extensive ablation study is conducted for the proposed approach. Comparison to previously published self-supervised denoising models demonstrates improved performance of the proposed framework, potentially serving as a preprocessing step towards superior segmentation performance and quantitative analysis.
Comment: 5 pages, 4 figures, 1 table, submitted to International Symposium on Biomedical Imaging 2024 |
| Document Type: |
Working Paper |
| Access URL: |
http://arxiv.org/abs/2311.15167 |
| Accession Number: |
edsarx.2311.15167 |
| Database: |
arXiv |
