MDFI-Net: Multiscale Differential Feature Interaction Network for Accurate Retinal Vessel Segmentation
This work addresses retinal vessel segmentation for medical imaging applications, presenting an incremental improvement over existing deep-learning methods.
The paper tackles the challenge of accurately segmenting retinal vessels in fundus images, which is difficult due to complex vessel structures and background interference, by proposing MDFI-Net, a feature-enhanced interaction network based on DPCN, achieving detection accuracies of 97.91%, 97.97%, and 98.16% on public datasets.
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.