论文标题
ADC-NET：一个开源深度学习网络，用于光学连贯性层析成像中的自动色散补偿
ADC-Net: An Open-Source Deep Learning Network for Automated Dispersion Compensation in Optical Coherence Tomography
论文作者
论文摘要
色散是降低光学相干断层扫描（OCT）中系统分辨率的常见问题。这项研究是为了开发一个十月的自动分散补偿（ADC-net）的深度学习网络。 ADC-NET基于重新设计的UNET体系结构，该体系结构采用编码器解码器管道。输入部分包括优化的单个视网膜层部分补偿OCT B扫描。相应的输出是所有视网膜层优化的完全补偿的OCT B扫描。使用了两个数字参数，即峰信号与噪声比（PSNR）和在多个尺度上计算的结构相似性指数（MS-SSSIM），用于对ADC-NET性能的客观评估。对培训模型的比较分析，包括单个，三个，五个，七和九输入通道。五输入渠道的实现被视为ADC-NET训练的最佳模式，以实现OCT的强大分散补偿
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 redesigned 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-scans 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. Comparative analysis of training models, including single, three, five, seven and nine input channels were implemented. The five-input channels implementation was observed as the optimal mode for ADC-Net training to achieve robust dispersion compensation in OCT
