Effectual accuracy of OCT image retinal segmentation with the aid of speckle noise reduction and boundary edge detection strategy

The Retinal Age Gap as a Marker of Accelerated Aging in the Early Course of Schizophrenia

BACKGROUND AND HYPOTHESIS

Given the available findings confirming accelerated brain aging in schizophrenia (SZ), we conducted a study aimed at verifying whether quantitative retinal morphological data enable age prediction and whether schizophrenia patients present with a positive retinal age gap (RAG).

STUDY DESIGN

Two samples of patients and controls were enrolled: one included 59 SZ patients and 60 controls, all of whom underwent optical coherence tomography (OCT) enabling the measurement of 72 variables. A second sample of 65 SZ patients and 70 controls was then combined with the first sample, to generate a database where each subject was represented by 28 morphological variables. Four different machine learning (ML) algorithms were used for age prediction based on z-standardized OCT data. The associations between RAG, demographic, and clinical data were also analyzed.

STUDY RESULTS

Patients from both samples had significantly higher retinal age and positive RAG ranging between 5.88 and 7.44 years depending on the specific sample. Predictions based on the larger group but with fewer OCT variables exhibited higher prediction relative error. All ML algorithms generated similar outcomes regarding retinal age. RAG correlated with the dose of antipsychotic medication and the severity of symptoms. Correlations with chronological age showed that RAG was the highest in younger patients, and from the age of about 45 years, it decreased.

CONCLUSIONS

ML-based results corroborated accelerated retinal aging in schizophrenia and showed its associations with pharmacological treatment and syndrome severity. The finding of a larger RAG in younger patients is novel and requires replication.

Employing texture loss to denoise OCT images using generative adversarial networks

OCT is a widely used clinical ophthalmic imaging technique, but the presence of speckle noise can obscure important pathological features and hinder accurate segmentation. This paper presents a novel method for denoising optical coherence tomography (OCT) images using a combination of texture loss and generative adversarial networks (GANs). Previous approaches have integrated deep learning techniques, starting with denoising Convolutional Neural Networks (CNNs) that employed pixel-wise losses. While effective in reducing noise, these methods often introduced a blurring effect in the denoised OCT images. To address this, perceptual losses were introduced, improving denoising performance and overall image quality. Building on these advancements, our research focuses on designing an image reconstruction GAN that generates OCT images with textural similarity to the gold standard, the averaged OCT image. We utilize the PatchGAN discriminator approach as a texture loss to enhance the quality of the reconstructed OCT images. We also compare the performance of UNet and ResNet as generators in the conditional GAN (cGAN) setting, as well as compare PatchGAN with the Wasserstein GAN. Using real clinical foveal-centered OCT retinal scans of children with normal vision, our experiments demonstrate that the combination of PatchGAN and UNet achieves superior performance (PSNR = 32.50) compared to recently proposed methods such as SiameseGAN (PSNR = 31.02). Qualitative experiments involving six masked clinical ophthalmologists also favor the reconstructed OCT images with PatchGAN texture loss. In summary, this paper introduces a novel method for denoising OCT images by incorporating texture loss within a GAN framework. The proposed approach outperforms existing methods and is well-received by clinical experts, offering promising advancements in OCT image reconstruction and facilitating accurate clinical interpretation.