The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Segmenting Retinal Blood Vessels WithDeep Neural Networks
Abstract
The condition of the vascular network of human eye is an important diagnostic factor in ophthalmology. Its segmentation in fundus imaging is a nontrivial task due to variable size of vessels, relatively low contrast, and potential presence of pathologies like microaneurysms and hemorrhages. Many algorithms, both unsupervised and supervised, have been proposed for this purpose in the past. We propose a supervised segmentation technique that uses a deep neural network trained on a large (up to 400[Formula: see text]000) sample of examples preprocessed with global contrast normalization, zero-phase whitening, and augmented using geometric transformations and gamma corrections. Several variants of the method are considered, including structured prediction, where a network classifies multiple pixels simultaneously. When applied to standard benchmarks of fundus imaging, the DRIVE, STARE, and CHASE databases, the networks significantly outperform the previous algorithms on the area under ROC curve measure (up to > 0.99) and accuracy of classification (up to > 0.97 ). The method is also resistant to the phenomenon of central vessel reflex, sensitive in detection of fine vessels ( sensitivity > 0.87 ), and fares well on pathological cases.
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Cites
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
Backpropagation Applied to Handwritten Zip Code Recognition
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Improving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
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Greedy layer-wise training of deep networks
Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007) · Advances in Neural Information Processing Systems 19 · doi:10.7551/mitpress/7503.003.0024 · Cited by 4,841
Ridge-based vessel segmentation in color images of the retina
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & van Ginneken, B. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2004.825627 · Cited by 4,202
Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., & Riedmiller, M. (2014) · arXiv · doi:10.48550/arxiv.1412.6806 · Cited by 3,447
Cited by
Deep Learning for Medical Image Processing: Overview, Challenges and the Future
Razzak, M. I., Naz, S., & Zaib, A. (2018) · Lecture Notes in Computational Vision and Biomechanics · doi:10.1007/978-3-319-65981-7_12 · Cited by 1,248
Embracing imperfect datasets: A review of deep learning solutions for medical image segmentation
Tajbakhsh, N., Jeyaseelan, L., Li, Q., Chiang, J. N., Wu, Z., & Ding, X. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101693 · Cited by 1,095
Weighted Res-UNet for High-Quality Retina Vessel Segmentation
Xiao, X., Lian, S., Luo, Z., & Li, S. (2018) · 2018 9th International Conference on Information Technology in Medicine and Education (ITME) · doi:10.1109/itme.2018.00080 · Cited by 963
A review of the application of deep learning in medical image classification and segmentation
Cai, L., Gao, J., & Zhao, D. (2020) · Annals of Translational Medicine · doi:10.21037/atm.2020.02.44 · Cited by 587
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.02.001 · Cited by 567
Metadata
- Authors
- Liskowski, P., & Krawiec, K.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-11
- Volume
- 35
- Issue
- 11
- Pages
- 2369-2380
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2327793514
- Cited by
- 890
- References
- 7
- FWCI
- 82.00
- Type
- journal article
- Language
- en
- Open access
- closed
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