Comparative analysis of alignment algorithms for macular optical coherence tomography imaging

doi:10.1186/s40942-023-00497-2

. 2023 Oct 2;9(1):60.

doi: 10.1186/s40942-023-00497-2.

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging

Craig K Jones^#^{1

2}, Bochong Li^#³, Jo-Hsuan Wu⁴, Toshiya Nakaguchi⁵, Ping Xuan⁶, T Y Alvin Liu^{7

8}

Affiliations

¹ Wilmer Eye Institute, School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
² The Malone Center for Engineering in Healthcare, Johns Hopkins University, Malone Hall, Suite 340, 3400 North Charles Street, Baltimore, MD, 21218, USA.
³ Graduate School of Science and Technology, Chiba University, 1-33, Yayoicho, Inage Ward, Chiba-shi, Chiba, 263-8522, Japan.
⁴ Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA, 92093, USA.
⁵ Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoicho, Inage Ward, Chiba-shi, Chiba, 263-8522, Japan.
⁶ School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.
⁷ Wilmer Eye Institute, School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA. tliu25@jhmi.edu.
⁸ The Malone Center for Engineering in Healthcare, Johns Hopkins University, Malone Hall, Suite 340, 3400 North Charles Street, Baltimore, MD, 21218, USA. tliu25@jhmi.edu.

^# Contributed equally.

PMID: 37784169
PMCID: PMC10544468
DOI: 10.1186/s40942-023-00497-2

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging

Craig K Jones et al. Int J Retina Vitreous. 2023.

. 2023 Oct 2;9(1):60.

doi: 10.1186/s40942-023-00497-2.

Authors

Craig K Jones^#^{1

2}, Bochong Li^#³, Jo-Hsuan Wu⁴, Toshiya Nakaguchi⁵, Ping Xuan⁶, T Y Alvin Liu^{7

8}

Affiliations

¹ Wilmer Eye Institute, School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA.
² The Malone Center for Engineering in Healthcare, Johns Hopkins University, Malone Hall, Suite 340, 3400 North Charles Street, Baltimore, MD, 21218, USA.
³ Graduate School of Science and Technology, Chiba University, 1-33, Yayoicho, Inage Ward, Chiba-shi, Chiba, 263-8522, Japan.
⁴ Shiley Eye Institute and Viterbi Family Department of Ophthalmology, University of California San Diego, 9415 Campus Point Drive, La Jolla, CA, 92093, USA.
⁵ Center for Frontier Medical Engineering, Chiba University, 1-33, Yayoicho, Inage Ward, Chiba-shi, Chiba, 263-8522, Japan.
⁶ School of Computer Science and Technology, Heilongjiang University, Harbin, 150080, China.
⁷ Wilmer Eye Institute, School of Medicine, Johns Hopkins University, 600 N. Wolfe Street, Baltimore, MD, 21287, USA. tliu25@jhmi.edu.
⁸ The Malone Center for Engineering in Healthcare, Johns Hopkins University, Malone Hall, Suite 340, 3400 North Charles Street, Baltimore, MD, 21218, USA. tliu25@jhmi.edu.

^# Contributed equally.

PMID: 37784169
PMCID: PMC10544468
DOI: 10.1186/s40942-023-00497-2

Abstract

Background: Optical coherence tomography (OCT) is the most important and commonly utilized imaging modality in ophthalmology and is especially crucial for the diagnosis and management of macular diseases. Each OCT volume is typically only available as a series of cross-sectional images (B-scans) that are accessible through proprietary software programs which accompany the OCT machines. To maximize the potential of OCT imaging for machine learning purposes, each OCT image should be analyzed en bloc as a 3D volume, which requires aligning all the cross-sectional images within a particular volume.

Methods: A dataset of OCT B-scans obtained from 48 age-related macular degeneration (AMD) patients and 50 normal controls was used to evaluate five registration algorithms. After alignment of B-scans from each patient, an en face surface map was created to measure the registration quality, based on an automatically generated Laplace difference of the surface map-the smoother the surface map, the smaller the average Laplace difference. To demonstrate the usefulness of B-scan alignment, we trained a 3D convolutional neural network (CNN) to detect age-related macular degeneration (AMD) on OCT images and compared the performance of the model with and without B-scan alignment.

Results: The mean Laplace difference of the surface map before registration was 27 ± 4.2 pixels for the AMD group and 26.6 ± 4 pixels for the control group. After alignment, the smoothness of the surface map was improved, with a mean Laplace difference of 5.5 ± 2.7 pixels for Advanced Normalization Tools Symmetric image Normalization (ANTs-SyN) registration algorithm in the AMD group and a mean Laplace difference of 4.3 ± 1.4.2 pixels for ANTs in the control group. Our 3D CNN achieved superior performance in detecting AMD, when aligned OCT B-scans were used (AUC 0.95 aligned vs. 0.89 unaligned).

Conclusions: We introduced a novel metric to quantify OCT B-scan alignment and compared the effectiveness of five alignment algorithms. We confirmed that alignment could be improved in a statistically significant manner with readily available alignment algorithms that are available to the public, and the ANTs algorithm provided the most robust performance overall. We further demonstrated that alignment of OCT B-scans will likely be useful for training 3D CNN models.

Keywords: Age-related macular degeneration; B-scans; Image alignment; Image registration; Optical coherence tomography.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

**Fig. 1**
Multiple OCT B-scans from the same patient scan (far left) are combined to form a 3D cube. The surface map is defined as the distance from the top of the data cube to the top of the nerve fiber layer (white arrow). It is expected that well aligned B-scans will result in a smooth surface map. The measure of the smoothness is defined as the mean value of the difference map which is created by applying a discrete Laplace operator over the surface map

**Fig. 2**
En face surface maps of OCT volumes. Each row represented a different patient, and each column represented a different alignment algorithm. The left most column was created from unregistered B-scans. (*AMD* Age-related Macular Degeneration)

**Fig. 3**
Mean of the Laplace difference of the surface map over all OCT volumes for each of the registration algorithms for control and AMD patients (top). The bottom panel only included OCT volumes with 61 B-Scans. Number above each box is the p-value from a paired t-test. (The circles in the figure are outliers and are discussed at the end of the paper)

**Fig. 4**
Mean number of edge errors over all OCT volumes for each of the registration algorithms for control and AMD patients (top). The bottom panel only included OCT volumes with 61 B-Scans

**Fig. 5**
(Left) visualization and comparisons of the surface maps created manually and automatically. (Right) Histograms showing the difference between each pair of surface maps in the number of pixels

**Fig. 6**
Average registration time in milliseconds per pair of B-scans for each algorithm

See this image and copyright information in PMC

Cited by

Artificial intelligence for diagnosing exudative age-related macular degeneration.
Kang C, Lo JE, Zhang H, Ng SM, Lin JC, Scott IU, Kalpathy-Cramer J, Liu SA, Greenberg PB. Kang C, et al. Cochrane Database Syst Rev. 2024 Oct 17;10(10):CD015522. doi: 10.1002/14651858.CD015522.pub2. Cochrane Database Syst Rev. 2024. PMID: 39417312
Adaptive Curved Slicing for En Face Imaging in Optical Coherence Tomography.
Li M, Loahavilai P, Liu Y, Li X, Li Y, Sun L. Li M, et al. Sensors (Basel). 2025 Jul 10;25(14):4329. doi: 10.3390/s25144329. Sensors (Basel). 2025. PMID: 40732457 Free PMC article.

References

1. Brenner DJ, Hall EJ. Computed tomography–an increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277–2284. doi: 10.1056/NEJMra072149. - DOI - PubMed
1. Huettel SA, Song AW, McCarthy G. Functional magnetic resonance imaging. Sunderland: Sinauer Associates; 2004.
1. Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, et al. Optical coherence tomography. Science. 1991;254(5035):1178–1181. doi: 10.1126/science.1957169. - DOI - PMC - PubMed
1. Budenz DL, Anderson DR, Varma R, Schuman J, Cantor L, Savell J, et al. Determinants of normal retinal nerve fiber layer thickness measured by stratus OCT. Ophthalmology. 2007;114(6):1046–1052. doi: 10.1016/j.ophtha.2006.08.046. - DOI - PMC - PubMed
1. Fujimoto JG, Brezinski ME, Tearney GJ, Boppart SA, Bouma B, Hee MR, et al. Optical biopsy and imaging using optical coherence tomography. Nat Med. 1995;1(9):970–972. doi: 10.1038/nm0995-970. - DOI - PubMed

LinkOut - more resources

Full Text Sources
Research Materials
- NCI CPTC Antibody Characterization Program
Miscellaneous
- NCI CPTAC Assay Portal

[1] Brenner DJ, Hall EJ. Computed tomography–an increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277–2284. doi: 10.1056/NEJMra072149. - DOI - PubMed

[2] Brenner DJ, Hall EJ. Computed tomography–an increasing source of radiation exposure. N Engl J Med. 2007;357(22):2277–2284. doi: 10.1056/NEJMra072149. - DOI - PubMed

[3] Huettel SA, Song AW, McCarthy G. Functional magnetic resonance imaging. Sunderland: Sinauer Associates; 2004.

[4] Huettel SA, Song AW, McCarthy G. Functional magnetic resonance imaging. Sunderland: Sinauer Associates; 2004.

[5] Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, et al. Optical coherence tomography. Science. 1991;254(5035):1178–1181. doi: 10.1126/science.1957169. - DOI - PMC - PubMed

[6] Huang D, Swanson EA, Lin CP, Schuman JS, Stinson WG, Chang W, et al. Optical coherence tomography. Science. 1991;254(5035):1178–1181. doi: 10.1126/science.1957169. - DOI - PMC - PubMed

[7] Budenz DL, Anderson DR, Varma R, Schuman J, Cantor L, Savell J, et al. Determinants of normal retinal nerve fiber layer thickness measured by stratus OCT. Ophthalmology. 2007;114(6):1046–1052. doi: 10.1016/j.ophtha.2006.08.046. - DOI - PMC - PubMed

[8] Budenz DL, Anderson DR, Varma R, Schuman J, Cantor L, Savell J, et al. Determinants of normal retinal nerve fiber layer thickness measured by stratus OCT. Ophthalmology. 2007;114(6):1046–1052. doi: 10.1016/j.ophtha.2006.08.046. - DOI - PMC - PubMed

[9] Fujimoto JG, Brezinski ME, Tearney GJ, Boppart SA, Bouma B, Hee MR, et al. Optical biopsy and imaging using optical coherence tomography. Nat Med. 1995;1(9):970–972. doi: 10.1038/nm0995-970. - DOI - PubMed

[10] Fujimoto JG, Brezinski ME, Tearney GJ, Boppart SA, Bouma B, Hee MR, et al. Optical biopsy and imaging using optical coherence tomography. Nat Med. 1995;1(9):970–972. doi: 10.1038/nm0995-970. - DOI - PubMed

Save citation to file

Email citation

Add to Collections

Add to My Bibliography

Your saved search

Create a file for external citation management software

Your RSS Feed

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging

Affiliations

Comparative analysis of alignment algorithms for macular optical coherence tomography imaging

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

LinkOut - more resources

Full Text Sources

Research Materials

Miscellaneous

Abstract

Conflict of interest statement

Figures

Similar articles

Cited by

References

Related information

LinkOut - more resources

Full Text Sources

Research Materials

Miscellaneous