ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography

Frontiers in Medicine

Guangying Ma¹  Taeyoon Son¹  Shaiban Ahmed¹  David Le¹  Tobiloba Adejumo¹  Xincheng Yao² 

[1] Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, United States;Department of Ophthalmology and Visual Science, University of Illinois Chicago, Chicago, IL, United States;

关键词: dispersion compensation;    deep learning;    fully convolutional network (FCN);    automated approach;    optical coherence tomography;   

DOI  :  10.3389/fmed.2022.864879

来源: DOAJ

【 摘 要 】

Chromatic dispersion is a common problem to degrade the system resolution in optical coherence tomography (OCT). This study is to develop a deep learning network for automated dispersion compensation (ADC-Net) in OCT. The ADC-Net is based on a modified UNet architecture which employs an encoder-decoder pipeline. The input section encompasses partially compensated OCT B-scans with individual retinal layers optimized. Corresponding output is a fully compensated OCT B-scan with all retinal layers optimized. Two numeric parameters, i.e., peak signal to noise ratio (PSNR) and structural similarity index metric computed at multiple scales (MS-SSIM), were used for objective assessment of the ADC-Net performance and optimal values of 29.95 ± 2.52 dB and 0.97 ± 0.014 were obtained respectively. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The mode with five-input channels was observed to be optimal for ADC-Net training to achieve robust dispersion compensation in OCT.

【 授权许可】

Unknown