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

3D Features Fusion for Automated Segmentation of Fluid Regions in CSCR Patients: An OCT-based Photodynamic Therapy Response Analysis

Central Serous Chorioretinopathy (CSCR) is a significant cause of vision impairment worldwide, with Photodynamic Therapy (PDT) emerging as a promising treatment strategy. The capability to precisely segment fluid regions in Optical Coherence Tomography (OCT) scans and predict the response to PDT treatment can substantially augment patient outcomes. This paper introduces a novel deep learning (DL) methodology for automated 3D segmentation of fluid regions in OCT scans, followed by a subsequent PDT response analysis for CSCR patients. Our approach utilizes the rich 3D contextual information from OCT scans to train a model that accurately delineates fluid regions. This model not only substantially reduces the time and effort required for segmentation but also offers a standardized technique, fostering further large-scale research studies. Additionally, by incorporating pre- and post-treatment OCT scans, our model is capable of predicting PDT response, hence enabling the formulation of personalized treatment strategies and optimized patient management. To validate our approach, we employed a robust dataset comprising 2,769 OCT scans (124 3D volumes), and the results obtained were significantly satisfactory, outperforming the current state-of-the-art methods. This research signifies an important milestone in the integration of DL advancements with practical clinical applications, propelling us a step closer towards improved management of CSCR. Furthermore, the methodologies and systems developed can be adapted and extrapolated to tackle similar challenges in the diagnosis and treatment of other retinal pathologies, favoring more comprehensive and personalized patient care.

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

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 ± 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.

Fuji sign: Prevalence and predictive power to photodynamic therapy in chronic central serous chorioretinopathy

AIM

To determine the prevalence of Fuji sign in central serous chorioretinopathy (cCSC) patients and its predictive power of good response to photodynamic therapy (PDT).

METHODS

Retrospective study, including 135 eyes of 130 patients diagnosed with cCSC treated with PDT between 2017 and 2021. Optical Coherence Tomography (OCT) images from these patients were compiled and analyzed. The presence of the Fuji sign, an anatomical finding recently described as a predictor of spontaneous resolution of the subretinal fluid (SRF) in cCSC, as assessed in basal images and the maximum height of SRF pre- and post-PDT OCT was measured.

RESULTS

Mean age was 56.6 years, 69.4% were men and the percentage of partial or complete resolution of the SRF after PDT was 75.55%. Only 8.9% of patients (12/135) had positive Fuji sign at baseline OCT. Among them, 50% (6/12) presented a complete response to the PDT (pre-PDT SRF: 109.00 (29.61) µm), 8.3% (1/12) had a partial resolution of the SRF (127 µm to 66 µm) and 41.6% (5/12) did not respond to PDT (pre-PDT SRF: 71.00 (22.82) µm, post-PDT SRF: 83.60 (36.13) µm).

CONCLUSIONS

Fuji sign has a low prevalence in cCSC and its presence is not associated with a good response to PDT.

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

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.

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

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.