The DOI matches its Crossref record; 160 of the 160 references it cites were found in a registry or an index.
Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics
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An overview of deep learning in medical imaging focusing on MRI
Lundervold, A. S., & Lundervold, A. (2019) · Zeitschrift für Medizinische Physik · doi:10.1016/j.zemedi.2018.11.002 · Cited by 2,058
Artificial intelligence in retina
Schmidt-Erfurth, U., Sadeghipour, A., Gerendas, B. S., Waldstein, S. M., & Bogunović, H. (2018) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2018.07.004 · Cited by 779
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
IterNet: Retinal Image Segmentation Utilizing Structural Redundancy in Vessel Networks
Li, L., Verma, M., Nakashima, Y., Nagahara, H., & Kawasaki, R. (2020) · 2020 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv45572.2020.9093621 · Cited by 357
Artificial intelligence promotes the diagnosis and screening of diabetic retinopathy
Huang, X., Wang, H., She, C., Feng, J., Liu, X., Hu, X., Chen, L., & Tao, Y. (2022) · Frontiers in Endocrinology · doi:10.3389/fendo.2022.946915 · Cited by 115
Metadata
- Authors
- Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S.
- Venue
- Computer Methods and Programs in Biomedicine
- Publisher
- Elsevier BV
- Published
- 2018-05
- Volume
- 158
- Pages
- 71-91
- ISSN
- 0169-2607 (ISSN-L)
- OpenAlex
- W2791117644
- Cited by
- 567
- References
- 160
- FWCI
- 29.91
- Type
- journal article
- Language
- en
- Open access
- green
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- Moccia, S., De Momi, E., El Hadji, S., & Mattos, L. S. (2018). Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics. Computer Methods and Programs in Biomedicine. https://doi.org/10.1016/j.cmpb.2018.02.001
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