# DPN: Detail-Preserving Network with High Resolution Representation for Efficient Segmentation of Retinal Vessels

arXiv: https://arxiv.org/abs/2009.12053 · HTML: https://mlsanity.com/paper/2009.12053/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2020.

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

- [MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation](https://mlsanity.com/paper/2210.08168/) (2022)
- [RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentation](https://mlsanity.com/paper/2112.11078/) (2021)
- [ESDMR-Net: A Lightweight Network With Expand-Squeeze and Dual Multiscale Residual Connections for Medical Image Segmentation](https://mlsanity.com/paper/2312.10585/) (2023)
- [Advancing Medical Image Segmentation with Mini-Net: A Lightweight Solution Tailored for Efficient Segmentation of Medical Images](https://mlsanity.com/paper/2405.17520/) (2024)
- [CGDD-Net: Context-Guided Dynamic Detail Modeling for Retinal Vessel Segmentation](https://mlsanity.com/paper/2610.05078/) (2026)

## Abstract (arXiv)

Retinal vessels are important biomarkers for many ophthalmological and cardiovascular diseases. Hence, it is of great significance to develop automatic models for computer-aided diagnosis. Existing methods, such as U-Net follow the encoder-decoder pipeline, where detailed information is lost in the encoder in order to achieve a large field of view. Although spatial detailed information could be recovered partly in the decoder, while there is noise in the high-resolution feature maps of the encoder. And, we argue this encoder-decoder architecture is inefficient for vessel segmentation. In this paper, we present the detail-preserving network (DPN), which avoids the encoder-decoder pipeline. To preserve detailed information and learn structural information simultaneously, we designed the detail-preserving block (DP-Block). Further, we stacked eight DP-Blocks together to form the DPN. More importantly, there are no down-sampling operations among these blocks. Therefore, the DPN could maintain a high/full resolution during processing, avoiding the loss of detailed information. To illustrate the effectiveness of DPN, we conducted experiments over three public datasets. Experimental results show, compared to state-of-the-art methods, DPN shows competitive/better performance in terms of segmentation accuracy, segmentation speed, and model size. Specifically, 1) Our method achieves comparable segmentation performance on the DRIVE, CHASE_DB1, and HRF datasets. 2) The segmentation speed of DPN is over 20-160 times faster than other methods on the DRIVE dataset. 3) The number of parameters of DPN is1 around 120k, far less than all comparison methods.