The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Retinal vessel segmentation based on Fully Convolutional Neural Networks
Open accessAbstract
Abstract The retinal vascular condition is a reliable biomarker of several ophthalmologic and cardiovascular diseases, so automatic vessel segmentation may be crucial to diagnose and monitor them. In this paper, we propose a novel method that combines the multiscale analysis provided by the Stationary Wavelet Transform with a multiscale Fully Convolutional Neural Network to cope with the varying width and direction of the vessel structure in the retina. Our proposal uses rotation operations as the basis of a joint strategy for both data augmentation and prediction, which allows us to explore the information learned during training to refine the segmentation. The method was evaluated on three publicly available databases, achieving an average accuracy of 0.9576, 0.9694, and 0.9653, and average area under the ROC curve of 0.9821, 0.9905, and 0.9855 on the DRIVE, STARE, and CHASE_DB1 databases, respectively. It also appears to be robust to the training set and to the inter-rater variability, which shows its potential for real-world applications.
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Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
(2014) · Cited by 33,419
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631
Proceedings of IEEE Conference on Computer Vision and Pattern Recognition
(1993) · doi:10.1109/cvpr.1993 · Cited by 17,667
Individual Comparisons by Ranking Methods
Wilcoxon, F. (1945) · Biometrics Bulletin · doi:10.2307/3001968 · Cited by 15,354
(2010) · Cited by 14,134
Cited by
Diabetic retinopathy detection through deep learning techniques: A review
Alyoubi, W. L., Shalash, W. M., & Abulkhair, M. F. (2020) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2020.100377 · Cited by 527
Applications of deep learning in fundus images: A review
Li, T., Bo, W., Hu, C., Kang, H., Liu, H., Wang, K., & Fu, H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.101971 · Cited by 396
Metadata
- Authors
- Oliveira, A., Pereira, S., & Silva, C. A.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2018-12
- Volume
- 112
- Pages
- 229-242
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2809144587
- Cited by
- 299
- References
- 61
- FWCI
- 20.63
- Type
- journal article
- Language
- en
- Open access
- green
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