The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Retinal layer segmentation of macular OCT images using boundary classification
Open accessAbstract
Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
Cortes, C., & Vapnik, V. (1995) · Machine Learning · doi:10.1007/bf00994018 · Cited by 59,421
A Computational Approach to Edge Detection
Canny, J. (1986) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.1986.4767851 · Cited by 30,004
An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
Supervised Machine Learning: A Review of Classification Techniques
Kotsiantis, S. B. (2007) · Informatica · Cited by 4,185
Improved boosting algorithms using confidence-rated predictions
Schapire, R. E., & Singer, Y. (1998) · Proceedings of the eleventh annual conference on Computational learning theory · doi:10.1145/279943.279960 · Cited by 2,547
Improved Boosting Algorithms Using Confidence-rated Predictions
Schapire, R. E., & Singer, Y. (1999) · Machine Learning · doi:10.1023/a:1007614523901 · Cited by 1,971
A Statistical Approach to Texture Classification from Single Images
Varma, M., & Zisserman, A. (2005) · International Journal of Computer Vision · doi:10.1007/s11263-005-4635-4 · Cited by 1,142
A role for local inflammation in the formation of drusen in the aging eye
Anderson, D. H., Mullins, R. F., Hageman, G. S., & Johnson, L. V. (2002) · American Journal of Ophthalmology · doi:10.1016/s0002-9394(02)01624-0 · Cited by 1,087
Cited by
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
Chiu, S. J., Allingham, M. J., Mettu, P. S., Cousins, S. W., Izatt, J. A., & Farsiu, S. (2015) · Biomedical Optics Express · doi:10.1364/boe.6.001172 · Cited by 377
Structured layer surface segmentation for retina OCT using fully convolutional regression networks
He, Y., Carass, A., Liu, Y., Jedynak, B. M., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101856 · Cited by 104
Self-citationSotirchos, E. S., Filippatou, A., Fitzgerald, K. C., Salama, S., Pardo, S., Wang, J., Ogbuokiri, E., Cowley, N. J., Pellegrini, N., Murphy, O. C., et al. (2020) · Multiple Sclerosis Journal · doi:10.1177/1352458519864928 · Cited by 98
Self-citationHe, Y., Carass, A., Solomon, S. D., Saidha, S., Calabresi, P. A., & Prince, J. L. (2019) · Data in Brief · doi:10.1016/j.dib.2018.12.073 · Cited by 90
Self-citation
Metadata
- Authors
- Lang, A., Carass, A., Hauser, M., Sotirchos, E. S., Calabresi, P. A., Ying, H. S., & Prince, J. L.
- Venue
- Biomedical Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2013-06-14
- Volume
- 4
- Issue
- 7
- Pages
- 1133
- ISSN
- 2156-7085 (ISSN-L)
- OpenAlex
- W2038850290
- Cited by
- 310
- References
- 38
- FWCI
- 11.88
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Retinal layer segmentation of macular OCT images using boundary classification. https://app.cochat.ai/papers/10.1364/boe.4.001133
- The paper
- Lang, A., Carass, A., Hauser, M., Sotirchos, E. S., Calabresi, P. A., Ying, H. S., & Prince, J. L. (2013). Retinal layer segmentation of macular OCT images using boundary classification. Biomedical Optics Express. https://doi.org/10.1364/boe.4.001133
Data sources
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.