# Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review

arXiv: https://arxiv.org/abs/2502.14935 · HTML: https://mlsanity.com/paper/2502.14935/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 132 references that are in the map.
- First posted on arXiv 20 Feb 2025.

## Builds on (most cited first, 18 of 18)

- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Applications of Deep Learning in Fundus Images: A Review](https://mlsanity.com/paper/2101.09864/) (2021)
- [OCTA-500: A Retinal Dataset for Optical Coherence Tomography Angiography Study](https://mlsanity.com/paper/2012.07261/) (2020)
- [Synthetic optical coherence tomography angiographs for detailed retinal vessel segmentation without human annotations](https://mlsanity.com/paper/2306.10941/) (2023)
- [FakeNews: GAN-based generation of realistic 3D volumetric data -- A systematic review and taxonomy](https://mlsanity.com/paper/2207.01390/) (2022)
- [Physiology-based simulation of the retinal vasculature enables annotation-free segmentation of OCT angiographs](https://mlsanity.com/paper/2207.11102/) (2022)
- [Automatic segmentation of the Foveal Avascular Zone in ophthalmological OCT-A images](https://mlsanity.com/paper/1811.10374/) (2018)
- [LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation](https://mlsanity.com/paper/2107.04282/) (2021)
- [A Deep Learning-based Quality Assessment and Segmentation System with a Large-scale Benchmark Dataset for Optical Coherence Tomographic Angiography Image](https://mlsanity.com/paper/2107.10476/) (2021)
- [Reconstruction of high-resolution 6x6-mm OCT angiograms using deep learning](https://mlsanity.com/paper/2004.08957/) (2020)
- [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)
- [Retinal Structure Detection in OCTA Image via Voting-based Multi-task Learning](https://mlsanity.com/paper/2208.10745/) (2022)
- [Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning](https://mlsanity.com/paper/2003.09033/) (2020)
- [3D Vessel Reconstruction in OCT-Angiography via Depth Map Estimation](https://mlsanity.com/paper/2102.13588/) (2021)
- [OCTAve: 2D en face Optical Coherence Tomography Angiography Vessel Segmentation in Weakly-Supervised Learning with Locality Augmentation](https://mlsanity.com/paper/2207.12238/) (2022)
- [Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data](https://mlsanity.com/paper/2311.03887/) (2023)
- [Self-Supervised Bulk Motion Artifact Removal in Optical Coherence Tomography Angiography](https://mlsanity.com/paper/2202.10360/) (2022)

## Abstract (arXiv)

Optical coherence tomography angiography (OCTA) is a non-invasive imaging technique widely used to study vascular structures and micro-circulation dynamics in the retina and choroid. OCTA has been widely used in clinics for diagnosing ocular disease and monitoring its progression, because OCTA is safer and faster than dye-based angiography while retaining the ability to characterize micro-scale structures. However, OCTA data contains many inherent noises from the devices and acquisition protocols and suffers from various types of artifacts, which impairs diagnostic accuracy and repeatability. Deep learning (DL) based imaging analysis models are able to automatically detect and remove artifacts and noises, and enhance the quality of image data. It is also a powerful tool for segmentation and identification of normal and pathological structures in the images. Thus, the value of OCTA imaging can be significantly enhanced by the DL-based approaches for interpreting and performing measurements and predictions on the OCTA data. In this study, we reviewed literature on the DL models for OCTA images in the latest five years. In particular, we focused on discussing the current problems in the OCTA data and the corresponding design principles of the DL models. We also reviewed the state-of-art DL models for 3D volumetric reconstruction of the vascular networks and pathological structures such as the edema and distorted optic disc. In addition, the publicly available dataset of OCTA images are summarized at the end of this review. Overall, this review can provide valuable insights for engineers to develop novel DL models by utilizing the characteristics of OCTA signals and images. The pros and cons of each DL methods and their applications discussed in this review can be helpful to assist technicians and clinicians to use proper DL models for fundamental research and disease screening.