OCTA-500: A retinal dataset for optical coherence tomography angiography study

Li, Mingchao; Huang, Kun; Xu, Qiuzhuo; Yang, Jiadong; Zhang, Yuhan; Ji, Zexuan; Xie, Keren; Yuan, Songtao; Liu, Qinghuai; Chen, Qiang

doi:10.1016/j.media.2024.103092

Cited by 29 publications

(1 citation statement)

References 84 publications

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“…Research in CV risk assessment using SS OCT-A was held back by the lack of data. In fact, only a few datasets such as the OCTAGON and FOCTAIR datasets 38 , the Retinal OCTA SEgmentation dataset (ROSE) 39 , and OCTA-500 40 were publicly available. However, none of these datasets combined the OCT-A scans with the CV data of the patients included.…”

Section: Machine-learning Model Resultsmentioning

confidence: 99%

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source microvascular imaging: the RASTA dataset

Germanese,

Anwer,

Eid

et al. 2024

Preprint

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The recent rise of artificial intelligence represents a revolutionary way of improving current medical practices, including cardiovascular (CV) assessment scores. Retinal vascular alterations may reflect systemic processes such as the presence of CV risk factors. The value of swept-source retinal optical coherence tomography–angiography (SS OCT-A) imaging is significantly enhanced by image analysis tools that provide rapid and accurate quantification of vascular features. We report on the interest of using machine-learning (ML) and deep-learning (DL) models for CV assessment from SS OCT-A microvasculature imaging. We assessed the accuracy of ML and DL algorithms in predicting the CHA2DS2-VASc neurocardiovascular score based on SS OCT-A retinal images of patients from the open-source RASTA dataset. The ML and DL models were trained on data from 491 patients. The ML models tested here achieved good performance with area under the curve (AUC) values ranging from 0.71 to 0.96. According to a classification into two or three CV risk groups, the EfficientNetV2-B3 tool predicted risk correctly in 39% and 68% of cases, respectively, with a mean absolute error (MAE) of approximately 0.697. Our models enable a confident prediction of the CHA2DS2-VASc score from SS OCT-A imaging, which could be a useful tool contributing to the assessment of neurocardiovascular profiles in the future.

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Section: Machine-learning Model Resultsmentioning

confidence: 99%

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source microvascular imaging: the RASTA dataset

Germanese,

Anwer,

Eid

et al. 2024

Preprint

View full text Add to dashboard Cite

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Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development

Li,

Wang,

Gao

et al. 2024

Autism Research

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Autism spectrum disorder (ASD) is a widely recognized neurodevelopmental disorder, yet the identification of reliable imaging biomarkers for its early diagnosis remains a challenge. Considering the specific manifestations of ASD in the eyes and the interconnectivity between the brain and the eyes, this study investigates ASD through the lens of retinal analysis. We specifically examined differences in the macular region of the retina using optical coherence tomography (OCT)/optical coherence tomography angiography (OCTA) images between children diagnosed with ASD and those with typical development (TD). Our findings present potential novel characteristics of ASD: the thickness of the ellipsoid zone (EZ) with cone photoreceptors was significantly increased in ASD; the large‐caliber arteriovenous of the inner retina was significantly reduced in ASD; these changes in the EZ and arteriovenous were more significant in the left eye than in the right eye. These observations of photoreceptor alterations, vascular function changes, and lateralization phenomena in ASD warrant further investigation, and we hope that this work can advance interdisciplinary understanding of ASD.

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SFNet: Spatial and Frequency Domain Networks for Wide‐Field OCT Angiography Retinal Vessel Segmentation

Li,

Ma,

Yan

et al. 2024

Journal of Biophotonics

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Automatic segmentation of blood vessels in fundus images is important to assist ophthalmologists in diagnosis. However, automatic segmentation for Optical Coherence Tomography Angiography (OCTA) blood vessels has not been fully investigated due to various difficulties, such as vessel complexity. In addition, there are only a few publicly available OCTA image data sets for training and validating segmentation algorithms. To address these issues, we constructed a wild‐field retinal OCTA segmentation data set, the Retinal Vessels Images in OCTA (REVIO) dataset. Second, we propose a new retinal vessel segmentation network based on spatial and frequency domain networks (SFNet). The proposed model are tested on three benchmark data sets including REVIO, ROSE and OCTA‐500. The experimental results show superior performance on segmentation tasks compared to the representative methods.

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OCTA-500: A retinal dataset for optical coherence tomography angiography study

Cited by 29 publications

References 84 publications

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source microvascular imaging: the RASTA dataset

Artificial intelligence-based prediction of neurocardiovascular risk score from retinal swept-source microvascular imaging: the RASTA dataset

Exploring autism via the retina: Comparative insights in children with autism spectrum disorder and typical development

SFNet: Spatial and Frequency Domain Networks for Wide‐Field OCT Angiography Retinal Vessel Segmentation

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