# MDFI-Net: Multiscale Differential Feature Interaction Network for Accurate Retinal Vessel Segmentation

arXiv: https://arxiv.org/abs/2410.15444 · HTML: https://mlsanity.com/paper/2410.15444/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 81 references that are in the map.
- First posted on arXiv 20 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Dilated Residual Networks](https://mlsanity.com/paper/1705.09914/) (2017)
- [clDice -- A Novel Topology-Preserving Loss Function for Tubular Structure Segmentation](https://mlsanity.com/paper/2003.07311/) (2020)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Topology-Preserving Deep Image Segmentation](https://mlsanity.com/paper/1906.05404/) (2019)
- [Beyond the Pixel-Wise Loss for Topology-Aware Delineation](https://mlsanity.com/paper/1712.02190/) (2017)
- [Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions](https://mlsanity.com/paper/1909.00166/) (2019)
- [ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model](https://mlsanity.com/paper/2007.05201/) (2020)
- [Topology-Aware Segmentation Using Discrete Morse Theory](https://mlsanity.com/paper/2103.09992/) (2021)
- [SA-UNet: Spatial Attention U-Net for Retinal Vessel Segmentation](https://mlsanity.com/paper/2004.03696/) (2020)
- [IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks](https://mlsanity.com/paper/1912.05763/) (2019)
- [CS2-Net: Deep Learning Segmentation of Curvilinear Structures in Medical Imaging](https://mlsanity.com/paper/2010.07486/) (2020)
- [Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learning](https://mlsanity.com/paper/2304.12856/) (2023)
- [Dual Cross-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2303.17696/) (2023)
- [Genetic U-Net: Automatically Designed Deep Networks for Retinal Vessel Segmentation Using a Genetic Algorithm](https://mlsanity.com/paper/2010.15560/) (2020)
- [BEFD: Boundary Enhancement and Feature Denoising for Vessel Segmentation](https://mlsanity.com/paper/2104.03768/) (2021)
- [Deep Neural Ensemble for Retinal Vessel Segmentation in Fundus Images towards Achieving Label-free Angiography](https://mlsanity.com/paper/1609.05871/) (2016)
- [Light-weight Retinal Layer Segmentation with Global Reasoning](https://mlsanity.com/paper/2404.16346/) (2024)

## Abstract (arXiv)

The accurate segmentation of retinal vessels in fundus images is a great challenge in medical image segmentation tasks due to their highly complex structure from other organs.Currently, deep-learning based methods for retinal cessel segmentation achieved suboptimal outcoms,since vessels with indistinct features are prone to being overlooked in deeper layers of the network. Additionally, the abundance of redundant information in the background poses significant interference to feature extraction, thus increasing the segmentation difficulty. To address this issue, this paper proposes a feature-enhanced interaction network based on DPCN, named MDFI-Net.Specifically, we design a feature enhancement structure, the Deformable-convolutional Pulse Coupling Network (DPCN), to provide an enhanced feature iteration sequence to the segmentation network in a simple and efficient manner. Subsequently, these features will interact within the segmentation network.Extensive experiments were conducted on publicly available retinal vessel segmentation datasets to validate the effectiveness of our network structure. Experimental results of our algorithm show that the detection accuracy of the retinal blood vessel achieves 97.91%, 97.97% and 98.16% across all datasets. Finally, plentiful experimental results also prove that the proposed MDFI-Net achieves segmentation performance superior to state-of-the-art methods on public datasets.