# IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks

arXiv: https://arxiv.org/abs/1912.05763 · HTML: https://mlsanity.com/paper/1912.05763/

## Key facts

- Cited by 57 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Dec 2019.

## Built on by (most cited first, 30 of 57)

- [FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation](https://mlsanity.com/paper/2103.17235/) (2021)
- [RV-GAN: Segmenting Retinal Vascular Structure in Fundus Photographs using a Novel Multi-scale Generative Adversarial Network](https://mlsanity.com/paper/2101.00535/) (2021)
- [Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels](https://mlsanity.com/paper/2103.03451/) (2021)
- [GT U-Net: A U-Net Like Group Transformer Network for Tooth Root Segmentation](https://mlsanity.com/paper/2109.14813/) (2021)
- [Semi-Supervised Semantic Segmentation of Vessel Images using Leaking Perturbations](https://mlsanity.com/paper/2110.11998/) (2021)
- [Local Intensity Order Transformation for Robust Curvilinear Object Segmentation](https://mlsanity.com/paper/2202.12587/) (2022)
- [RAVIR: A Dataset and Methodology for the Semantic Segmentation and Quantitative Analysis of Retinal Arteries and Veins in Infrared Reflectance Imaging](https://mlsanity.com/paper/2203.14928/) (2022)
- [DTU-Net: Learning Topological Similarity for Curvilinear Structure Segmentation](https://mlsanity.com/paper/2205.11115/) (2022)
- [Defending Medical Image Diagnostics against Privacy Attacks using Generative Methods](https://mlsanity.com/paper/2103.03078/) (2021)
- [Exploring The Limits Of Data Augmentation For Retinal Vessel Segmentation](https://mlsanity.com/paper/2105.09365/) (2021)
- [PointScatter: Point Set Representation for Tubular Structure Extraction](https://mlsanity.com/paper/2209.05774/) (2022)
- [Double Encoder-Decoder Networks for Gastrointestinal Polyp Segmentation](https://mlsanity.com/paper/2110.01939/) (2021)
- [BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation](https://mlsanity.com/paper/2104.03768/) (2021)
- [SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Image](https://mlsanity.com/paper/2207.13882/) (2022)
- [AOSLO-net: A deep learning-based method for automatic segmentation of retinal microaneurysms from adaptive optics scanning laser ophthalmoscope images](https://mlsanity.com/paper/2106.02800/) (2021)
- [RSF-Conv: Rotation-and-Scale Equivariant Fourier Parameterized Convolution for Retinal Vessel Segmentation](https://mlsanity.com/paper/2309.15638/) (2023)
- [Denoising Diffusion Probabilistic Model for Retinal Image Generation and Segmentation](https://mlsanity.com/paper/2308.08339/) (2023)
- [VascX Models: Model Ensembles for Retinal Vascular Analysis from Color Fundus Images](https://mlsanity.com/paper/2409.16016/) (2024)
- [EAR-NET: Error Attention Refining Network For Retinal Vessel Segmentation](https://mlsanity.com/paper/2107.01351/) (2021)
- [Retinal Vessel Segmentation with Pixel-wise Adaptive Filters](https://mlsanity.com/paper/2202.01782/) (2022)
- [Curvilinear object segmentation in medical images based on ODoS filter and deep learning network](https://mlsanity.com/paper/2301.07475/) (2023)
- [Drawing the Line: Deep Segmentation for Extracting Art from Ancient Etruscan Mirrors](https://mlsanity.com/paper/2404.15903/) (2024)
- [LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio](https://mlsanity.com/paper/2509.11811/) (2025)
- [Learning Enhancement From Degradation: A Diffusion Model For Fundus Image Enhancement](https://mlsanity.com/paper/2303.04603/) (2023)
- [SCOPE: Structural Continuity Preservation for Medical Image Segmentation](https://mlsanity.com/paper/2304.14572/) (2023)
- [FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation](https://mlsanity.com/paper/2307.07245/) (2023)
- [Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization](https://mlsanity.com/paper/2406.14994/) (2024)
- [Attention W-Net: Improved Skip Connections for better Representations](https://mlsanity.com/paper/2110.08811/) (2021)
- [MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation](https://mlsanity.com/paper/2204.03213/) (2022)
- [IterMiUnet: A lightweight architecture for automatic blood vessel segmentation](https://mlsanity.com/paper/2208.01485/) (2022)

## Abstract (arXiv)

Retinal vessel segmentation is of great interest for diagnosis of retinal vascular diseases. To further improve the performance of vessel segmentation, we propose IterNet, a new model based on UNet, with the ability to find obscured details of the vessel from the segmented vessel image itself, rather than the raw input image. IterNet consists of multiple iterations of a mini-UNet, which can be 4× deeper than the common UNet. IterNet also adopts the weight-sharing and skip-connection features to facilitate training; therefore, even with such a large architecture, IterNet can still learn from merely 10~20 labeled images, without pre-training or any prior knowledge. IterNet achieves AUCs of 0.9816, 0.9851, and 0.9881 on three mainstream datasets, namely DRIVE, CHASE-DB1, and STARE, respectively, which currently are the best scores in the literature. The source code is available.