# LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation

arXiv: https://arxiv.org/abs/2107.04282 · HTML: https://mlsanity.com/paper/2107.04282/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 34 references that are in the map.
- First posted on arXiv 9 Jul 2021.

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

- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head](https://mlsanity.com/paper/1809.10589/) (2018)
- [Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography](https://mlsanity.com/paper/1609.05871/) (2016)
- [Automated Segmentation of Optical Coherence Tomography Angiography Images: Benchmark Data and Clinically Relevant Metrics](https://mlsanity.com/paper/1912.09978/) (2019)

## Built on by (most cited first, 9 of 9)

- [Unsupervised Denoising of Retinal OCT with Diffusion Probabilistic Model](https://mlsanity.com/paper/2201.11760/) (2022)
- [Cats: Complementary CNN and Transformer Encoders for Segmentation](https://mlsanity.com/paper/2208.11572/) (2022)
- [Simulation-Based Segmentation of Blood Vessels in Cerebral 3D OCTA Images](https://mlsanity.com/paper/2403.07116/) (2024)
- [MAP: Domain Generalization via Meta-Learning on Anatomy-Consistent Pseudo-Modalities](https://mlsanity.com/paper/2309.01286/) (2023)
- [Deep Angiogram: Trivializing Retinal Vessel Segmentation](https://mlsanity.com/paper/2307.00245/) (2023)
- [Convolutional Prompting for Broad-Domain Retinal Vessel Segmentation](https://mlsanity.com/paper/2412.18089/) (2024)
- [VesselMorph: Domain-Generalized Retinal Vessel Segmentation via Shape-Aware Representation](https://mlsanity.com/paper/2307.00240/) (2023)
- [CATS v2: Hybrid encoders for robust medical segmentation](https://mlsanity.com/paper/2308.06377/) (2023)
- [Denoising, segmentation and volumetric rendering of optical coherence tomography angiography (OCTA) image using deep learning techniques: a review](https://mlsanity.com/paper/2502.14935/) (2025)

## Abstract (arXiv)

Optical coherence tomography (OCT) is a non-invasive imaging technique widely used for ophthalmology. It can be extended to OCT angiography (OCT-A), which reveals the retinal vasculature with improved contrast. Recent deep learning algorithms produced promising vascular segmentation results; however, 3D retinal vessel segmentation remains difficult due to the lack of manually annotated training data. We propose a learning-based method that is only supervised by a self-synthesized modality named local intensity fusion (LIF). LIF is a capillary-enhanced volume computed directly from the input OCT-A. We then construct the local intensity fusion encoder (LIFE) to map a given OCT-A volume and its LIF counterpart to a shared latent space. The latent space of LIFE has the same dimensions as the input data and it contains features common to both modalities. By binarizing this latent space, we obtain a volumetric vessel segmentation. Our method is evaluated in a human fovea OCT-A and three zebrafish OCT-A volumes with manual labels. It yields a Dice score of 0.7736 on human data and 0.8594 +/- 0.0275 on zebrafish data, a dramatic improvement over existing unsupervised algorithms.