Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy

Xie, Sha; Okuwobi, Idowu Paul; Li, Mingchao; Zhang, Yuhan; Yuan, Songtao; Chen, Qiang

doi:10.1167/tvst.9.2.21

Cited by 21 publications

(15 citation statements)

References 19 publications

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“…More complicated evaluation procedures require even more assistance from AI. In another study, Okuwobi and colleagues developed a DLS that automatically quantified HF and could inform physicians about the size, number, and location of HF with an average dice similarity coefficient of 71.3% 25,75 . A large-scale prospective study based on similar system is expected to end the controversy over HF.…”

Section: Review Methodologymentioning

confidence: 99%

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Zhang

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Diabetic macular edema (DME) is the primary cause of central vision impairment in patients with diabetes and the leading cause of preventable blindness in working-age people. With the advent of optical coherence tomography and antivascular endothelial growth factor (anti-VEGF) therapy, the diagnosis, evaluation, and treatment of DME were greatly revolutionized in the last decade. However, there is tremendous heterogeneity among DME patients, and 30%–50% of DME patients do not respond well to anti-VEGF agents. In addition, there is no evidence-based and universally accepted administration regimen. The identification of DME patients not responding to anti-VEGF agents and the determination of the optimal administration interval are the 2 major challenges of DME, which are difficult to achieve with the coarse granularity of conventional health care modality. Therefore, more and more retina specialists have pointed out the necessity of introducing precision medicine into the management of DME and have conducted related studies in recent years. One of the most frontier methods is the targeted extraction of individualized disease features from optical coherence tomography images based on artificial intelligence technology, which provides precise evaluation and risk classification of DME. This review aims to provide an overview of the progress of artificial intelligence-enabled precision medicine in automated screening, precise evaluation, prognosis prediction, and follow-up monitoring of DME. Further, the challenges ahead of real-world applications and the future development of precision medicine in DME will be discussed.

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Section: Review Methodologymentioning

confidence: 99%

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Zhang

et al. 2023

Asia-Pacific Journal of Ophthalmology

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“…In some papers, HRF represent all hyperreflective lesions in SD-OCT B-scans, including HRDs and hard exudates (HE). [13][14][15][16][17][18][19][20] HE are lipid deposits and refer to the block-shaped areas with high local contrast and high reflectivity in SD-OCT B-scans. The diameter of HE is larger than 40 µm.In other papers, HRF represent spot-shaped areas with high reflectivity in SD-OCT B-scans, which is the same as HRDs.…”

Section: Introductionmentioning

confidence: 99%

“…The term hyperreflective foci (HRF) in previous papers is vague. In some papers, HRF represent all hyperreflective lesions in SD‐OCT B‐scans, including HRDs and hard exudates (HE) 13–20 . HE are lipid deposits and refer to the block‐shaped areas with high local contrast and high reflectivity in SD‐OCT B‐scans.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

Zhang

Zhao

et al. 2022

Medical Physics

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Purpose Hyperreflective dots (HRDs) can be observed in spectral domain optical coherence tomography (SD‐OCT), which can provide a sensitive marker in the treatment decision process. Quantitative analyses of HRDs are the key to make appropriate decisions on observation, treatment, and retreatment. The purpose of this study is to automatically and accurately segment HRDs in SD‐OCT B‐scans with diabetic retinopathy (DR). Methods The authors propose an automatic segmentation algorithm of HRDs via focal priors and visual saliency. The algorithm is divided into three stages: segmentation of retinal layers, calculation of the multiscale local contrast saliency map, and adaptive threshold segmentation. First, a method based on improved graph search is used to segment retinal layers to obtain the region of interest (ROI) and the reflectivity estimation of the retinal pigment epithelium (RPE) layer; then, the multiscale local contrast saliency map is obtained by using a local contrast measure, which measures the dissimilarity between the current pixels and corresponding neighborhoods; finally, an adaptive threshold is applied to segment HRDs. Results Experimental results on 20 SD‐OCT B‐scans demonstrate that our method is effective for HRDs segmentation. The average dice similarity coefficient (DSC) and detection accuracy are 71.12% and 85.07%, respectively. Conclusions The proposed method can accurately segment HRDs in SD‐OCT B‐scans with DR and outperforms current state‐of‐the‐art methods. Our method can provide reliable HRDs segmentation to assist ophthalmologists in clinical diagnosis, treatment, disease monitoring, and progression.

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“…In the same year, Gao et al used ResNet as the backbone network in U-Net++ [20], redesigned the skip connection structure, used ResNeSt [21] to improve the synthesized structure, studied the edema region in OCT images, and obtained good segmentation results [22]. In the same year, Xie et al used image enhancement and improved 3D U-Net to implement a fast and automatic hyper-reflection focus segmentation method, and also obtained good segmentation results [23]. In 2021, Acevedo-Jake et al used anti-angiogenic peptide hydrogel and pro-angiogenic peptide hydrogel to treat neovascular age macular degeneration [24].…”

Section: Introductionmentioning

confidence: 99%

Research on Semantic Segmentation Method of Macular Edema in Retinal OCT Images Based on Improved Swin-Unet

Gao

Chen

2022

Electronics

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Optical coherence tomography (OCT), as a new type of tomography technology, has the characteristics of non-invasive, real-time imaging and high sensitivity, and is currently an important medical imaging tool to assist ophthalmologists in the screening, diagnosis, and follow-up treatment of patients with macular disease. In order to solve the problem of irregular occurrence area of diabetic retinopathy macular edema (DME), multi-scale and multi-region cluster of macular edema, which leads to inaccurate segmentation of the edema area, an improved Swin-Unet networks model was proposed for automatic semantic segmentation of macular edema lesion areas in OCT images. Firstly, in the deep bottleneck of the Swin-Unet network, the Resnet network layer was used to increase the extraction of pairs of sub-feature images. Secondly, the Swin Transformer block and skip connection structure were used for global and local learning, and the regions after semantic segmentation were morphologically smoothed and post-processed. Finally, the proposed method was performed on the macular edema patient dataset publicly available at Duke University, and was compared with previous segmentation methods. The experimental results show that the proposed method can not only improve the overall semantic segmentation accuracy of retinal macular edema, but also further to improve the semantic segmentation effect of multi-scale and multi-region edema regions.

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Fast and Automated Hyperreflective Foci Segmentation Based on Image Enhancement and Improved 3D U-Net in SD-OCT Volumes with Diabetic Retinopathy

Cited by 21 publications

References 19 publications

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Application of Artificial Intelligence in Precision Medicine for Diabetic Macular Edema

Automatic segmentation of hyperreflective dots via focal priors and visual saliency

Research on Semantic Segmentation Method of Macular Edema in Retinal OCT Images Based on Improved Swin-Unet

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