# Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography

arXiv: https://arxiv.org/abs/1609.05871 · HTML: https://mlsanity.com/paper/1609.05871/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2016.

## Built on by (most cited first, 5 of 5)

- [Ensemble deep learning: A review](https://mlsanity.com/paper/2104.02395/) (2021)
- [LIFE: A Generalizable Autodidactic Pipeline for 3D OCT-A Vessel Segmentation](https://mlsanity.com/paper/2107.04282/) (2021)
- [Texture Aware Autoencoder Pre-training And Pairwise Learning Refinement For Improved Iris Recognition](https://mlsanity.com/paper/2202.07499/) (2022)
- [A comprehensive survey on computer-aided diagnostic systems in diabetic retinopathy screening](https://mlsanity.com/paper/2208.01810/) (2022)
- [MDFI-Net: Multiscale Differential Feature Interaction Network for Accurate Retinal Vessel Segmentation](https://mlsanity.com/paper/2410.15444/) (2024)

## Abstract (arXiv)

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member auto-encoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708 . Comparison with other major algorithms substantiates the high efficacy of our model.