Deep Learning for Detecting Subretinal Fluid and Discerning Macular Status by Fundus Images in Central Serous Chorioretinopathy

Xu, Fabao; Liu, Shaopeng; Xiang, Yifan; Lin, Zhenzhe; Liu, Cong; Zhou, Lijun; Gong, Yuanyuan; Li, Longhui; Li, Zhongwen; Guo, Chong; Huang, Chuangxin; Lai, Kunbei; Zhao, Hongkun; Hong, Jiaming; Lin, Haotian; Jin, Chenjin

doi:10.3389/fbioe.2021.651340

Cited by 6 publications

(4 citation statements)

References 21 publications

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“…In previous studies, deep learning models for fundus photography domain only classified pathological cases for CSC diagnosis but did not focus on SRF lesion detection. 4 , 8 In our experiment using a small dataset ( Fig. 8 ), the conventional saliency map based on Grad-CAM refers to larger areas than the actual SRF and it is unable to highlight the detailed features of CSC.…”

Section: Discussionmentioning

confidence: 80%

“… 33 To evaluate the severity of CSC in fundus photography, a deep learning model was developed to classify the SRF into macula-on or macula-off subretinal detachment. 8 However, because previous studies were based on classification algorithms, the quantitative evaluation of retinal involvement of SRF lesions could not be performed in the fundus photography domain. Our proposed segmentation model may overcome drawbacks of the previous studies that investigated CSC using fundus photography.…”

Section: Discussionmentioning

confidence: 99%

“… 4 However, previous studies using deep learning have not shown the ability to identify detailed pathological lesions to monitor the conditions of SRF in the fundus photographs with CSC. 4 , 8 Recently, deep learning–based segmentation tasks have improved the diagnostic and decision-making process in various imaging domains. 9 U-Net is a well-established deep learning architecture for the detection and segmentation tasks for biomedical imaging domains.…”

Section: Introductionmentioning

confidence: 99%

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Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Yoo

Kim

Jeong

et al. 2022

Trans. Vis. Sci. Tech.

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Purpose Central serous chorioretinopathy (CSC) is a retinal disease that frequently shows resolution and recurrence with serous detachment of the neurosensory retina. Here, we present a deep learning analysis of subretinal fluid (SRF) lesion segmentation in fundus photographs to evaluate CSC. Methods We collected 194 fundus photographs of SRF lesions from the patients with CSC. Three graders manually annotated of the entire SRF area in the retinal images. The dataset was randomly separated into training (90%) and validation (10%) datasets. We used the U-Net segmentation model based on conditional generative adversarial networks (pix2pix) to detect the SRF lesions. The algorithms were trained and validated using Google Colaboratory. Researchers did not need prior knowledge of coding skills or computing resources to implement this code. Results The validation results showed that the Jaccard index and Dice coefficient scores were 0.619 and 0.763, respectively. In most cases, the segmentation results overlapped with most of the reference areas in the annotated images. However, cases with exceptional SRFs were not accurate in terms of prediction. Using Colaboratory, the proposed segmentation task ran easily in a web-based environment without setup or personal computing resources. Conclusions The results suggest that the deep learning model based on U-Net from the pix2pix algorithm is suitable for the automatic segmentation of SRF lesions to evaluate CSC. Translational Relevance Our code implementation has the potential to facilitate ophthalmology research; in particular, deep learning–based segmentation can assist in the development of pathological lesion detection solutions.

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Section: Discussionmentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Yoo

Kim

Jeong

et al. 2022

Trans. Vis. Sci. Tech.

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“…Meanwhile, the application of artificial intelligence (AI) in the medical field has become increasingly popular in recent years ( Caixinha and Nunes, 2017 ). The past decade has witnessed several inroads achieved with AI being harnessed for learning and mining fundus image data, assisting doctors in screening, diagnosing, and treating various retinopathies ( Xu et al, 2021b ; Ting et al, 2021 ). Generative adversarial network (GAN), first proposed by Ian Goodfellow in 2014, is an AI-based “image-to-image” algorithm that can synthesize new images based on existing ones ( Goodfellow et al, 2016 ; Xu et al, 2021a ).…”

Section: Introductionmentioning

confidence: 99%

Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network

Yu²,

Gao³

et al. 2022

Front. Bioeng. Biotechnol.

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To generate and evaluate post-therapeutic optical coherence tomography (OCT) images based on pre-therapeutic images with generative adversarial network (GAN) to predict the short-term response of patients with retinal vein occlusion (RVO) to anti-vascular endothelial growth factor (anti-VEGF) therapy. Real-world imaging data were retrospectively collected from 1 May 2017, to 1 June 2021. A total of 515 pairs of pre-and post-therapeutic OCT images of patients with RVO were included in the training set, while 68 pre-and post-therapeutic OCT images were included in the validation set. A pix2pixHD method was adopted to predict post-therapeutic OCT images in RVO patients after anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated by screening and evaluation experiments. We quantitatively and qualitatively assessed the prognostic accuracy of the synthetic post-therapeutic OCT images. The post-therapeutic OCT images generated by the pix2pixHD algorithm were comparable to the actual images in edema resorption response. Retinal specialists found most synthetic images (62/68) difficult to differentiate from the real ones. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic and real OCT images was 26.33 ± 15.81 μm. There was no statistical difference in CMT between the synthetic and the real images. In this retrospective study, the application of the pix2pixHD algorithm objectively predicted the short-term response of each patient to anti-VEGF therapy based on OCT images with high accuracy, suggestive of its clinical value, especially for screening patients with relatively poor prognosis and potentially guiding clinical treatment. Importantly, our artificial intelligence-based prediction approach’s non-invasiveness, repeatability, and cost-effectiveness can improve compliance and follow-up management of this patient population.

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Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images

Gende,

Castelo,

de Moura

et al. 2024

J Digit Imaging. Inform. med.

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Central Serous Chorioretinopathy (CSC) is a retinal disorder caused by the accumulation of fluid, resulting in vision distortion. The diagnosis of this disease is typically performed through Optical Coherence Tomography (OCT) imaging, which displays any fluid buildup between the retinal layers. Currently, these fluid regions are manually detected by visual inspection a time-consuming and subjective process that can be prone to errors. A series of six deep learning-based automatic segmentation architectural configurations of different levels of complexity were trained and compared in order to determine the best model intended for the automatic segmentation of CSC-related lesions in OCT images. The best performing models were then evaluated in an external validation study. Furthermore, an intra- and inter-expert analysis was conducted in order to compare the manual segmentation performed by expert ophthalmologists with the automatic segmentation provided by the models. Test results of the best performing configuration achieved a mean Dice of $$0.868 \mathop {\pm } 0.056$$ 0.868 ± 0.056 in the internal dataset. In the external validation set, these models achieved a level of agreement with human experts of up to 0.960 in terms of Kappa coefficient, contrasting with a value of 0.951 for agreement between human experts. Overall, the models reached a better agreement with either of the human experts than these experts with each other, suggesting that automatic segmentation models for the detection of CSC-related lesions in OCT imaging can be useful tools for assessing this disease, reducing the workload of manual inspection and leading to a more robust and objective diagnosis method.

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Deep Learning for Detecting Subretinal Fluid and Discerning Macular Status by Fundus Images in Central Serous Chorioretinopathy

Cited by 6 publications

References 21 publications

Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Predicting OCT images of short-term response to anti-VEGF treatment for retinal vein occlusion using generative adversarial network

Intra- and Inter-expert Validation of an Automatic Segmentation Method for Fluid Regions Associated with Central Serous Chorioretinopathy in OCT Images

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