# LadderNet: Multi-path networks based on U-Net for medical image segmentation

arXiv: https://arxiv.org/abs/1810.07810 · HTML: https://mlsanity.com/paper/1810.07810/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Oct 2018.

## Built on by (most cited first, 29 of 29)

- [RFormer: Transformer-based Generative Adversarial Network for Real Fundus Image Restoration on A New Clinical Benchmark](https://mlsanity.com/paper/2201.00466/) (2022)
- [Study Group Learning: Improving Retinal Vessel Segmentation Trained with Noisy Labels](https://mlsanity.com/paper/2103.03451/) (2021)
- [A Survey of Historical Document Image Datasets](https://mlsanity.com/paper/2203.08504/) (2022)
- [SineNet: Learning Temporal Dynamics in Time-Dependent Partial Differential Equations](https://mlsanity.com/paper/2403.19507/) (2024)
- [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)
- [Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes](https://mlsanity.com/paper/2104.03577/) (2021)
- [BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation](https://mlsanity.com/paper/2104.03768/) (2021)
- [DR-VNet: Retinal Vessel Segmentation via Dense Residual UNet](https://mlsanity.com/paper/2111.04739/) (2021)
- [Self-adversarial Multi-scale Contrastive Learning for Semantic Segmentation of Thermal Facial Images](https://mlsanity.com/paper/2209.10700/) (2022)
- [LGPS: A Lightweight GAN-Based Approach for Polyp Segmentation in Colonoscopy Images](https://mlsanity.com/paper/2503.18294/) (2025)
- [SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss](https://mlsanity.com/paper/2202.09515/) (2022)
- [Exploiting CNNs for Semantic Segmentation with Pascal VOC](https://mlsanity.com/paper/2304.13216/) (2023)
- [SCOPE: Structural Continuity Preservation for Medical Image Segmentation](https://mlsanity.com/paper/2304.14572/) (2023)
- [Benchmarking Retinal Blood Vessel Segmentation Models for Cross-Dataset and Cross-Disease Generalization](https://mlsanity.com/paper/2406.14994/) (2024)
- [Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images](https://mlsanity.com/paper/2103.13622/) (2021)
- [Attention W-Net: Improved Skip Connections for better Representations](https://mlsanity.com/paper/2110.08811/) (2021)
- [UNet Architectures in Multiplanar Volumetric Segmentation -- Validated on Three Knee MRI Cohorts](https://mlsanity.com/paper/2203.08194/) (2022)
- [MC-UNet Multi-module Concatenation based on U-shape Network for Retinal Blood Vessels Segmentation](https://mlsanity.com/paper/2204.03213/) (2022)
- [Segmentation of Blood Vessels, Optic Disc Localization, Detection of Exudates and Diabetic Retinopathy Diagnosis from Digital Fundus Images](https://mlsanity.com/paper/2207.04345/) (2022)
- [A BERT-Style Self-Supervised Learning CNN for Disease Identification from Retinal Images](https://mlsanity.com/paper/2504.18049/) (2025)
- [A State-of-the-art Survey of U-Net in Microscopic Image Analysis: from Simple Usage to Structure Mortification](https://mlsanity.com/paper/2202.06465/) (2022)
- [DoubleU-NetPlus: A Novel Attention and Context Guided Dual U-Net with Multi-Scale Residual Feature Fusion Network for Semantic Segmentation of Medical Images](https://mlsanity.com/paper/2211.14235/) (2022)
- [Artificial intelligence as a gateway to scientific discovery: Uncovering features in retinal fundus images](https://mlsanity.com/paper/2301.06675/) (2023)
- [Deep Learning Methods for Retinal Blood Vessel Segmentation: Evaluation on Images with Retinopathy of Prematurity](https://mlsanity.com/paper/2306.11576/) (2023)
- [MCPA: Multi-scale Cross Perceptron Attention Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/2307.14588/) (2023)
- [Pruning random resistive memory for optimizing analogue AI](https://mlsanity.com/paper/2311.07164/) (2023)
- [Deep Wave Network for Modeling Multi-Scale Physical Dynamics](https://mlsanity.com/paper/2605.04198/) (2026)
- [Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis](https://mlsanity.com/paper/2607.17691/) (2026)
- [CGDD-Net: Context-Guided Dynamic Detail Modeling for Retinal Vessel Segmentation](https://mlsanity.com/paper/2610.05078/) (2026)

## Abstract (arXiv)

U-Net has been providing state-of-the-art performance in many medical image segmentation problems. Many modifications have been proposed for U-Net, such as attention U-Net, recurrent residual convolutional U-Net (R2-UNet), and U-Net with residual blocks or blocks with dense connections. However, all these modifications have an encoder-decoder structure with skip connections, and the number of paths for information flow is limited. We propose LadderNet in this paper, which can be viewed as a chain of multiple U-Nets. Instead of only one pair of encoder branch and decoder branch in U-Net, a LadderNet has multiple pairs of encoder-decoder branches, and has skip connections between every pair of adjacent decoder and decoder branches in each level. Inspired by the success of ResNet and R2-UNet, we use modified residual blocks where two convolutional layers in one block share the same weights. A LadderNet has more paths for information flow because of skip connections and residual blocks, and can be viewed as an ensemble of Fully Convolutional Networks (FCN). The equivalence to an ensemble of FCNs improves segmentation accuracy, while the shared weights within each residual block reduce parameter number. Semantic segmentation is essential for retinal disease detection. We tested LadderNet on two benchmark datasets for blood vessel segmentation in retinal images, and achieved superior performance over methods in the literature. The implementation is provided urlhttps://github.com/juntang-zhuang/LadderNet