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

arXiv: https://arxiv.org/abs/2201.12625 · HTML: https://mlsanity.com/paper/2201.12625/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 39 references that are in the map.
- First posted on arXiv 29 Jan 2022.

## Builds on (most cited first, 4 of 4)

- [A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head](https://mlsanity.com/paper/1809.10589/) (2018)
- [AV-Net: Deep learning for fully automated artery-vein classification in optical coherence tomography angiography](https://mlsanity.com/paper/2006.03742/) (2020)
- [Automated segmentation of retinal fluid volumes from structural and angiographic optical coherence tomography using deep learning](https://mlsanity.com/paper/2006.02569/) (2020)
- [Supervised machine learning based multi-task artificial intelligence classification of retinopathies](https://mlsanity.com/paper/1905.04224/) (2019)

## Abstract (arXiv)

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