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MFI-Net: A multi-resolution fusion input network for retinal vessel segmentation
- Source :
- PLoS ONE, PLoS ONE, Vol 16, Iss 7, p e0253056 (2021)
- Publication Year :
- 2021
-
Abstract
- Segmentation of retinal vessels is important for doctors to diagnose some diseases. The segmentation accuracy of retinal vessels can be effectively improved by using deep learning methods. However, most of the existing methods are incomplete for shallow feature extraction, and some superficial features are lost, resulting in blurred vessel boundaries and inaccurate segmentation of capillaries in the segmentation results. At the same time, the “layer-by-layer” information fusion between encoder and decoder makes the feature information extracted from the shallow layer of the network cannot be smoothly transferred to the deep layer of the network, resulting in noise in the segmentation features. In this paper, we propose the MFI-Net (Multi-resolution fusion input network) network model to alleviate the above problem to a certain extent. The multi-resolution input module in MFI-Net avoids the loss of coarse-grained feature information in the shallow layer by extracting local and global feature information in different resolutions. We have reconsidered the information fusion method between the encoder and the decoder, and used the information aggregation method to alleviate the information isolation between the shallow and deep layers of the network. MFI-Net is verified on three datasets, DRIVE, CHASE_DB1 and STARE. The experimental results show that our network is at a high level in several metrics, with F1 higher than U-Net by 2.42%, 2.46% and 1.61%, higher than R2U-Net by 1.47%, 2.22% and 0.08%, respectively. Finally, this paper proves the robustness of MFI-Net through experiments and discussions on the stability and generalization ability of MFI-Net.
- Subjects :
- Computer science
02 engineering and technology
030218 nuclear medicine & medical imaging
Machine Learning
0302 clinical medicine
Mathematical and Statistical Techniques
0202 electrical engineering, electronic engineering, information engineering
Image Processing, Computer-Assisted
Medicine and Health Sciences
Segmentation
Network model
Multidisciplinary
Feature (computer vision)
Medicine
020201 artificial intelligence & image processing
Anatomy
Encoder
Algorithms
Network Analysis
Network analysis
Research Article
Computer and Information Sciences
Neural Networks
Fundus Oculi
Imaging Techniques
Science
Ocular Anatomy
Feature extraction
Research and Analysis Methods
Models, Biological
03 medical and health sciences
Deep Learning
Robustness (computer science)
Ocular System
Artificial Intelligence
Humans
business.industry
Deep learning
Retinal Vessels
Biology and Life Sciences
Pattern recognition
Convolution
Capillaries
Cardiovascular Anatomy
Blood Vessels
Artificial intelligence
business
Mathematical Functions
Neuroscience
Subjects
Details
- ISSN :
- 19326203
- Volume :
- 16
- Issue :
- 7
- Database :
- OpenAIRE
- Journal :
- PloS one
- Accession number :
- edsair.doi.dedup.....b2480ee0210e5018fa2a5793a680fa7e