# RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/2112.11078 · HTML: https://mlsanity.com/paper/2112.11078/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 21 Dec 2021.

## Builds on (most cited first, 10 of 10)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Searching for MobileNetV3](https://mlsanity.com/paper/1905.02244/) (2019)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design](https://mlsanity.com/paper/1807.11164/) (2018)
- [A Closer Look at Few-shot Classification](https://mlsanity.com/paper/1904.04232/) (2019)
- [A Metric Learning Reality Check](https://mlsanity.com/paper/2003.08505/) (2020)
- [Deep Retinal Image Understanding](https://mlsanity.com/paper/1609.01103/) (2016)
- [Retinal vessel segmentation based on Fully Convolutional Neural Networks](https://mlsanity.com/paper/1812.07110/) (2018)
- [DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels](https://mlsanity.com/paper/2009.12053/) (2020)

## Built on by (most cited first, 12 of 12)

- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.08168/) (2022)
- [ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation](https://mlsanity.com/paper/2312.10585/) (2023)
- [TBConvL-Net: A Hybrid Deep Learning Architecture for Robust Medical Image Segmentation](https://mlsanity.com/paper/2409.03367/) (2024)
- [LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation](https://mlsanity.com/paper/2309.04968/) (2023)
- [Neural Network Compression by Joint Sparsity Promotion and Redundancy Reduction](https://mlsanity.com/paper/2210.07451/) (2022)
- [LFRA-Net: A Lightweight Focal and Region-Aware Attention Network for Retinal Vessel Segmentatio](https://mlsanity.com/paper/2509.11811/) (2025)
- [Deep Learning for Vascular Segmentation and Applications in Phase Contrast Tomography Imaging](https://mlsanity.com/paper/2311.13319/) (2023)
- [LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms](https://mlsanity.com/paper/2306.06145/) (2023)
- [Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation](https://mlsanity.com/paper/2510.20933/) (2025)
- [A Residual Encoder-Decoder Network for Segmentation of Retinal Image-Based Exudates in Diabetic Retinopathy Screening](https://mlsanity.com/paper/2201.05963/) (2022)
- [EDDense-Net: Fully Dense Encoder Decoder Network for Joint Segmentation of Optic Cup and Disc](https://mlsanity.com/paper/2308.10192/) (2023)

## Abstract (arXiv)

Over recent years, increasingly complex approaches based on sophisticated convolutional neural network architectures have been slowly pushing performance on well-established benchmark datasets. In this paper, we take a step back to examine the real need for such complexity. We present RC-Net, a fully convolutional network, where the number of filters per layer is optimized to reduce feature overlapping and complexity. We also used skip connections to keep spatial information loss to a minimum by keeping the number of pooling operations in the network to a minimum. Two publicly available retinal vessel segmentation datasets were used in our experiments. In our experiments, RC-Net is quite competitive, outperforming alternatives vessels segmentation methods with two or even three orders of magnitude less trainable parameters.