Semantic Segmentation of the Choroid in Swept Source Optical Coherence Tomography Images for Volumetrics

Tsuji, Shingo; Sekiryu, Tetsuju; Sugano, Yukinori; Ojima, Akira; Kasai, Akihito; Okamoto, Masahiro; Eifuku, Satoshi

doi:10.1038/s41598-020-57788-z

Cited by 24 publications

(16 citation statements)

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A further advantage of the SS-OCT devices is that the longer wavelength of the light source allows for deeper penetration of the choroid, resolving choroidal structures relevant to several sight-threatening diseases. Deep learning algorithms, that harness SS-OCT’s ability to penetrate the choroid, have already been developed and can segment the choroidal-scleral boundary to quantify choroidal volume [ 27 , 28 ].…”

Section: Discussionmentioning

confidence: 99%

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Sodhi

Pereira

Oakley³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

Purpose To evaluate the predictive ability of a deep learning-based algorithm to determine long-term best-corrected distance visual acuity (BCVA) outcomes in neovascular age-related macular degeneration (nARMD) patients using baseline swept-source optical coherence tomography (SS-OCT) and OCT-angiography (OCT-A) data. Methods In this phase IV, retrospective, proof of concept, single center study, SS-OCT data from 17 previously treated nARMD eyes was used to assess retinal layer thicknesses, as well as quantify intraretinal fluid (IRF), subretinal fluid (SRF), and serous pigment epithelium detachments (PEDs) using a novel deep learning-based, macular fluid segmentation algorithm. Baseline OCT and OCT-A morphological features and fluid measurements were correlated using the Pearson correlation coefficient (PCC) to changes in BCVA from baseline to week 52. Results Total retinal fluid (IRF, SRF and PED) volume at baseline had the strongest correlation to improvement in BCVA at month 12 (PCC = 0.652, p = 0.005). Fluid was subsequently sub-categorized into IRF, SRF and PED, with PED volume having the next highest correlation (PCC = 0.648, p = 0.005) to BCVA improvement. Average total retinal thickness in isolation demonstrated poor correlation (PCC = 0.334, p = 0.189). When two features, mean choroidal neovascular membranes (CNVM) size and total fluid volume, were combined and correlated with visual outcomes, the highest correlation increased to PCC = 0.695 (p = 0.002). Conclusions In isolation, total fluid volume most closely correlates with change in BCVA values between baseline and week 52. In combination with complimentary information from OCT-A, an improvement in the linear correlation score was observed. Average total retinal thickness provided a lower correlation, and thus provides a lower predictive outcome than alternative metrics assessed. Clinically, a machine-learning approach to analyzing fluid metrics in combination with lesion size may provide an advantage in personalizing therapy and predicting BCVA outcomes at week 52.

show abstract

Section: Discussionmentioning

confidence: 99%

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Sodhi

Pereira

Oakley³

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…For this work, an encoder depth of four was used for the SegNet networks. This network architecture has also been applied to choroidal 38 and fluid segmentation 39 in ocular OCT images.…”

Section: Methodsmentioning

confidence: 99%

Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images

Muller

Alonso‐Caneiro

Read

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

Purpose The purpose of this study was to develop a deep learning model for automatic binarization of the choroidal tissue, separating choroidal blood vessels from nonvascular stromal tissue, in optical coherence tomography (OCT) images from healthy young subjects. Methods OCT images from an observational longitudinal study of 100 children were used for training, validation, and testing of 5 fully semantic networks, which provided a binarized output of the choroid. These outputs were compared with ground truth images, generated from a local binarization technique after manually optimizing the analysis window size for each individual image. The performance was evaluated using accuracy and repeatability metrics. The methods were also compared with a fixed window size local binarization technique, which has been commonly used previously. Results The tested deep learning methods provided a good performance in terms of accuracy and repeatability. With the U-Net and SegNet networks showing >96% accuracy. All methods displayed a high level of repeatability relative to the ground truth. For analysis of the choroidal vascularity index (a commonly used metric derived from the binarized image), SegNet showed the closest agreement with the ground truth and high repeatability. The fixed window size showed a reduced accuracy compared to other methods. Conclusions Fully semantic networks such as U-Net and SegNet displayed excellent performance for the binarization task. These methods provide a useful approach for clinical and research applications of deep learning tools for the binarization of the choroid in OCT images. Translational Relevance Deep learning models provide a novel, robust solution to automatically binarize the choroidal tissue in OCT images.

show abstract

“…Numerous segmentation algorithms, in both automatic and semi-automatic manners, have been developed, including k-nearest mean (77), graph cut ( 78 Recently developed retinal layer segmentation algorithms have been heavily influenced by machine learning (ML) concepts. Various ML methods including support vector machine (88), random forest (89), neural network (90), and DL architectures were applied to retinal segmentation in healthy and diseased eyes (91)(92)(93)(94)(95)(96)(97)(98)(99)(100)(101). De Fauw and coworkers (102) were able to provide precise segmentation of multiple pathological sites after training on 14,884 scans.…”

Section: Bmentioning

confidence: 99%

Approaches to quantify optical coherence tomography angiography metrics

Tan¹,

Sim²,

Chua³

et al. 2020

Ann Transl Med

View full text Add to dashboard Cite

Optical coherence tomography (OCT) has revolutionized the field of ophthalmology in the last three decades. As an OCT extension, OCT angiography (OCTA) utilizes a fast OCT system to detect motion contrast in ocular tissue and provides a three-dimensional representation of the ocular vasculature in a non-invasive, dye-free manner. The first OCT machine equipped with OCTA function was approved by U.S. Food and Drug Administration in 2016 and now it is widely applied in clinics. To date, numerous methods have been developed to aid OCTA interpretation and quantification. In this review, we focused on the workflow of OCTA-based interpretation, beginning from the generation of the OCTA images using signal decorrelation, which we divided into intensity-based, phase-based and phasor-based methods. We further discussed methods used to address image artifacts that are commonly observed in clinical settings, to the algorithms for image enhancement, binarization, and OCTA metrics extraction. We believe a better grasp of these technical aspects of OCTA will enhance the understanding of the technology and its potential application in disease diagnosis and management. Moreover, future studies will also explore the use of ocular OCTA as a window to link ocular vasculature to the function of other organs such as the kidney and brain.

show abstract

Semantic Segmentation of the Choroid in Swept Source Optical Coherence Tomography Images for Volumetrics

Cited by 24 publications

References 31 publications

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Utilization of deep learning to quantify fluid volume of neovascular age-related macular degeneration patients based on swept-source OCT imaging: The ONTARIO study

Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images

Approaches to quantify optical coherence tomography angiography metrics

Contact Info

Product

Resources

About