The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images
Abstract
This paper presents a new supervised method for vessel segmentation in retinal images. This method remolds the task of segmentation as a problem of cross-modality data transformation from retinal image to vessel map. A wide and deep neural network with strong induction ability is proposed to model the transformation, and an efficient training strategy is presented. Instead of a single label of the center pixel, the network can output the label map of all pixels for a given image patch. Our approach outperforms reported state-of-the-art methods in terms of sensitivity, specificity and accuracy. The result of cross-training evaluation indicates its robustness to the training set. The approach needs no artificially designed feature and no preprocessing step, reducing the impact of subjective factors. The proposed method has the potential for application in image diagnosis of ophthalmologic diseases, and it may provide a new, general, high-performance computing framework for image segmentation.
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Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001
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
(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051
Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response
Hoover, A. D., Kouznetsova, V., & Goldbaum, M. (2000) · IEEE Transactions on Medical Imaging · doi:10.1109/42.845178 · Cited by 2,582
Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification
Soares, J. V. B., Leandro, J. J. G., Cesar, R. M., Jelinek, H. F., & Cree, M. J. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879967 · Cited by 1,497
An Ensemble Classification-Based Approach Applied to Retinal Blood Vessel Segmentation
Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2012.2205687 · Cited by 1,031
Blood vessel segmentation methodologies in retinal images – A survey
Fraz, M. M., Remagnino, P., Hoppe, A., Uyyanonvara, B., Rudnicka, A. R., Owen, C. G., & Barman, S. A. (2012) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2012.03.009 · Cited by 1,001
Marín, D., Aquino, A., Gegundez-Arias, M. E., & Bravo, J. M. (2011) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2010.2064333 · Cited by 929
Mendonca, A. M., & Campilho, A. (2006) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.879955 · Cited by 921
Cited by
DUNet: A deformable network for retinal vessel segmentation
Jin, Q., Meng, Z., Pham, T. D., Chen, Q., Wei, L., & Su, R. (2019) · Knowledge-Based Systems · doi:10.1016/j.knosys.2019.04.025 · Cited by 867
Recurrent residual U-Net for medical image segmentation
Alom, M. Z., Yakopcic, C., Hasan, M., Taha, T. M., & Asari, V. K. (2019) · Journal of Medical Imaging · doi:10.1117/1.jmi.6.1.014006 · Cited by 850
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
Fang, L., Cunefare, D., Wang, C., Guymer, R. H., Li, S., & Farsiu, S. (2017) · Biomedical Optics Express · doi:10.1364/boe.8.002732 · Cited by 481
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
Azad, R., Asadi-Aghbolaghi, M., Fathy, M., & Escalera, S. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00052 · Cited by 474
Metadata
- Authors
- Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., & Wang, T.
- Venue
- IEEE Transactions on Medical Imaging
- Published
- 2016-01
- Volume
- 35
- Issue
- 1
- Pages
- 109-118
- ISSN
- 0278-0062 (ISSN-L)
- 1558-254X
- OpenAlex
- W2206167351
- Cited by
- 571
- References
- 33
- FWCI
- 28.82
- Type
- journal article
- Language
- en
- Open access
- closed
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- Li, Q., Feng, B., Xie, L., Liang, P., Zhang, H., & Wang, T. (2016). A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/tmi.2015.2457891
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