Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Brea, Luisa Sánchez; Jesus, Danilo Andrade De; Shirazi, Muhammad Faizan; Pircher, Michael; Walsum, Theo van; Klein, Stefan

doi:10.3390/app9132700

Cited by 21 publications

(32 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…OCTA requires a very high sampling density in order to achieve the resolution needed to detect the tiny capillaries found in the retina. Its current limitations include inability to show leakage, proclivity for image artifacts due to patient movement/blinking, 54 and a relatively small field of view (e.g., 3 x 3 mm, 4.5 x 4.5 mm, and 6 x 6 mm). Hence, studies incident on glaucoma diagnosis and progression have been focused on specific regions of interest, namely the macula and the optic disc, including the peripapillary region.…”

Section: Discussionmentioning

confidence: 99%

OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

Jesus

Brea

Breda

et al. 2020

Trans. Vis. Sci. Tech.

Self Cite

View full text Add to dashboard Cite

To develop and assess an automatic procedure for classifying and staging glaucomatous vascular damage based on optical coherence tomography angiography (OCTA) imaging. Methods: OCTA scans (Zeiss Cirrus 5000 HD-OCT) from a random eye of 39 healthy subjects and 82 glaucoma patients were used to develop a new classification algorithm based on multilayer and multisector information. The averaged circumpapillary retinal nerve fiber layer (RNFL) thickness was also collected. Three models, support vector machine (SVM), random forest (RF), and gradient boosting (xGB), were developed and optimized for classifying between healthy and glaucoma patients, primary open-angle glaucoma (POAG) and normal-tension glaucoma (NTG), and glaucoma severity groups. Results: All the models, the SVM (area under the receiver operating characteristic [AUROC] 0.89 ± 0.06), the RF (AUROC 0.86 ± 0.06), and the xGB (AUROC 0.85 ± 0.07), with 26, 22, and 29 vascular features obtained after feature selection, respectively, presented a similar performance to the RNFL thickness (AUROC 0.85± 0.06) in classifying healthy and glaucoma patients. The superficial vascular plexus was the most informative layer with the infero temporal sector as the most discriminative region of interest. No significant differentiation was obtained in discriminating the POAG from the NTG group. The xGB model, after feature selection, presented the best performance in classifying the severity groups (AUROC 0.76± 0.06), outperforming the RNFL (AUROC 0.67± 0.06). Conclusions: OCTA multilayer and multisector information has similar performance to RNFL for glaucoma diagnosis, but it has an added value for glaucoma severity classification, showing promising results for staging glaucoma progression. Translational Relevance: OCTA, in its current stage, has the potential to be used in clinical practice as a complementary imaging technique in glaucoma management.

show abstract

Section: Discussionmentioning

confidence: 99%

OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

Jesus

Brea

Breda

et al. 2020

Trans. Vis. Sci. Tech.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The automatic tool is clinically effective in segmenting retinal images, and identification might improve accurate diagnosis by non-retinal experts; therefore, the application of automatic tools to analysis of retinal images could provide an alternative solution for large-scale fundus images screening, especially in areas with limited access to ophthalmologic experts [6]. However, the automatic segmentation of retinal images is not an easy task, and several factors, including light exposure, camera focus, motion artifact, and existing diseases, can hamper the image quality [54][55][56]. These potential factors are often responsible for inhomogeneous image quality and, thus, hamper vessel segmentation.…”

Section: Research and Clinical Implicationsmentioning

confidence: 99%

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Islam

Poly

Walther

et al. 2020

JCM

View full text Add to dashboard Cite

Background and Objective: Accurate retinal vessel segmentation is often considered to be a reliable biomarker of diagnosis and screening of various diseases, including cardiovascular diseases, diabetic, and ophthalmologic diseases. Recently, deep learning (DL) algorithms have demonstrated high performance in segmenting retinal images that may enable fast and lifesaving diagnoses. To our knowledge, there is no systematic review of the current work in this research area. Therefore, we performed a systematic review with a meta-analysis of relevant studies to quantify the performance of the DL algorithms in retinal vessel segmentation. Methods: A systematic search on EMBASE, PubMed, Google Scholar, Scopus, and Web of Science was conducted for studies that were published between 1 January 2000 and 15 January 2020. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) procedure. The DL-based study design was mandatory for a study’s inclusion. Two authors independently screened all titles and abstracts against predefined inclusion and exclusion criteria. We used the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool for assessing the risk of bias and applicability. Results: Thirty-one studies were included in the systematic review; however, only 23 studies met the inclusion criteria for the meta-analysis. DL showed high performance for four publicly available databases, achieving an average area under the ROC of 0.96, 0.97, 0.96, and 0.94 on the DRIVE, STARE, CHASE_DB1, and HRF databases, respectively. The pooled sensitivity for the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.77, 0.79, 0.78, and 0.81, respectively. Moreover, the pooled specificity of the DRIVE, STARE, CHASE_DB1, and HRF databases was 0.97, 0.97, 0.97, and 0.92, respectively. Conclusion: The findings of our study showed the DL algorithms had high sensitivity and specificity for segmenting the retinal vessels from digital fundus images. The future role of DL algorithms in retinal vessel segmentation is promising, especially for those countries with limited access to healthcare. More compressive studies and global efforts are mandatory for evaluating the cost-effectiveness of DL-based tools for retinal disease screening worldwide.

show abstract

“…A major challenge in OCT imaging is the axial and horizontal motion artifacts introduced by involuntary eye movements. Even when the patient fixates upon a fixed object, the eye still carries out small and rapid movements including rapid microsaccades, high frequency tremors, and slow drifts with various frequency and magnitude [4]. However, even with high-speed Spectral Domain-OCT (SD-OCT) devices that can acquire more than 300,000 A-scans per second [5], it typically takes several seconds to acquire a 3D volume [6].…”

Section: Introductionmentioning

confidence: 99%

“…However, even with high-speed Spectral Domain-OCT (SD-OCT) devices that can acquire more than 300,000 A-scans per second [5], it typically takes several seconds to acquire a 3D volume [6]. Therefore, artifacts due to the fixational eye movements are almost inevitable [4]. These involuntary eye movements would introduce both axial and coronal distortion, which leads to discontinuity in display and visualization of the 3D OCT data as shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

Learning to Correct Axial Motion in Oct for 3D Retinal Imaging

Wang

Warter

Cavichini

et al. 2021

2021 IEEE International Conference on Image Processing (ICIP)

View full text Add to dashboard Cite

Optical Coherence Tomography (OCT) is a powerful technique for non-invasive 3D imaging of biological tissues at high resolution that has revolutionized retinal imaging. A major challenge in OCT imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose a convolutional neural network that learns to correct axial motion in OCT based on a single volumetric scan. The proposed method is able to correct large motion, while preserving the overall curvature of the retina. The experimental results show significant improvements in visual quality as well as overall error compared to the conventional methods in both normal and disease cases.

show abstract

Review on Retrospective Procedures to Correct Retinal Motion Artefacts in OCT Imaging

Cited by 21 publications

References 115 publications

OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

OCTA Multilayer and Multisector Peripapillary Microvascular Modeling for Diagnosing and Staging of Glaucoma

Artificial Intelligence in Ophthalmology: A Meta-Analysis of Deep Learning Models for Retinal Vessels Segmentation

Learning to Correct Axial Motion in Oct for 3D Retinal Imaging

Contact Info

Product

Resources

About