The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal
Open accessAbstract
This paper concerns the validation of automatic retinal image analysis (ARIA) algorithms. For reasons of space and consistency, we concentrate on the validation of algorithms processing color fundus camera images, currently the largest section of the ARIA literature. We sketch the context (imaging instruments and target tasks) of ARIA validation, summarizing the main image analysis and validation techniques. We then present a list of recommendations focusing on the creation of large repositories of test data created by international consortia, easily accessible via moderated Web sites, including multicenter annotations by multiple experts, specific to clinical tasks, and capable of running submitted software automatically on the data stored, with clear and widely agreed-on performance criteria, to provide a fair comparison.
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Cites
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
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
A Steady-Hand Robotic System for Microsurgical Augmentation
Taylor, R., Jensen, P., Whitcomb, L., Barnes, A., Kumar, R., Stoianovici, D., Gupta, P., Wang, Z., Dejuan, E., & Kavoussi, L. (1999) · The International Journal of Robotics Research · doi:10.1177/02783649922067807 · Cited by 528
Evaluation of diagnostic tests when there is no gold standard. A review of methods
Rutjes, A., Reitsma, J., Coomarasamy, A., Khan, K., & Bossuyt, P. (2007) · Health Technology Assessment · doi:10.3310/hta11500 · Cited by 524
Cost-Effectiveness of Interventions to Prevent and Control Diabetes Mellitus: A Systematic Review
Li, R., Zhang, P., Barker, L. E., Chowdhury, F. M., & Zhang, X. (2010) · Diabetes Care · doi:10.2337/dc10-0843 · Cited by 491
Saaddine, J. B. (2008) · Archives of Ophthalmology · doi:10.1001/archopht.126.12.1740 · Cited by 377
Cited by
Abràmoff, M. D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J. C., & Niemeijer, M. (2016) · Investigative Opthalmology & Visual Science · doi:10.1167/iovs.16-19964 · Cited by 1,101
Self-citationOrlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
Screening for diabetic retinopathy: new perspectives and challenges
Vujosevic, S., Aldington, S. J., Silva, P., Hernández, C., Scanlon, P., Peto, T., & Simó, R. (2020) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(19)30411-5 · Cited by 671
An ensemble deep learning based approach for red lesion detection in fundus images
Orlando, J. I., Prokofyeva, E., del Fresno, M., & Blaschko, M. B. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2017.10.017 · Cited by 293
Pellegrini, E., Ballerini, L., Hernandez, M. D. C. V., Chappell, F. M., González‐Castro, V., Anblagan, D., Danso, S., Muñoz‐Maniega, S., Job, D., Pernet, C., et al. (2018) · Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring · doi:10.1016/j.dadm.2018.07.004 · Cited by 292
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Metadata
- Authors
- Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., al-Diri, B., Cheung, C. Y., Wong, D., Abràmoff, M., Lim, G., Kumar, D., Burlina, P., Bressler, N. M., Jelinek, H. F., Meriaudeau, F., Quellec, G., MacGillivray, T., & Dhillon, B.
- Venue
- Investigative Opthalmology & Visual Science
- Published
- 2013-05-24
- Volume
- 54
- Issue
- 5
- Pages
- 3546
- ISSN
- 0146-0404 (ISSN-L)
- 1552-5783
- OpenAlex
- W2095405787
- Cited by
- 164
- References
- 6
- FWCI
- 10.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Trucco, E., Ruggeri, A., Karnowski, T., Giancardo, L., Chaum, E., Hubschman, J. P., al-Diri, B., Cheung, C. Y., Wong, D., Abràmoff, M., Lim, G., Kumar, D., Burlina, P., Bressler, N. M., Jelinek, H. F., Meriaudeau, F., Quellec, G., MacGillivray, T., & Dhillon, B. (2013). Validating Retinal Fundus Image Analysis Algorithms: Issues and a Proposal. Investigative Opthalmology & Visual Science. https://doi.org/10.1167/iovs.12-10347
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
