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Introduction <p>This study addresses the use of zero-shot learning (ZSL) for segmentation of the foveal avascular zone (FAZ) in optical coherence tomography (OCT) images obtained through the RedCheck<sup>®</sup> platform. Accurate FAZ segmentation is essential for ophthalmologic diagnoses in conditions such as diabetic retinopathy and age-related macular degeneration. The proposed method aims to overcome the limitation of labeled data, reducing both the cost and time associated with model training.</p> Methods <p>A total of 200 images from healthy patients were used. A neural network-based model was employed to identify the FAZ without specific labeled data, using pre-trained representations for contextual learning. Model performance was evaluated by comparing the automatic segmentation results with the manual annotations provided by specialists.</p> Results <p>Quantitative analysis revealed a mean intersection over union (MIoU) of 0.86, indicating consistent model performance in identifying regions of interest. The median IoU was 0.89, with an interquartile range between 0.85 (Q1) and 0.92 (Q3), demonstrating the method’s precision in most samples. Extreme values showed a maximum IOU of 0.97, reflecting excellent agreement, whereas the minimum IoU of 0.03 revealed limitations in atypical cases. The standard deviation of 0.11 indicated moderate variation in the results, and the 95% confidence interval for the MIoU ranged from 0.84 to 0.89, ensuring the statistical reliability of the approach.</p> Discussion <p>The findings demonstrate the feasibility and accuracy of the ZSL-based method for FAZ segmentation, even in the absence of labeled data. Despite the positive results, variability observed in specific images highlights the need for improvements to increase the model’s robustness in more heterogeneous data scenarios.</p>

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Semantic segmentation of the avascular zone of the fovea in optical coherence tomography angiography: evaluation of techniques and applications in ocular diseases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年10月15日

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  • Brena Fernanda de Sousa Carvalho,
  • Alexandre Antônio Marques Rosa,
  • Rafael Scherer,
  • Valberto Monteiro Nunes,
  • Francisco Vinícius Moraes de Souza,
  • José Leandro Nascimento da Silva,
  • Taurino dos Santos Rodrigues Neto

摘要

Introduction

This study addresses the use of zero-shot learning (ZSL) for segmentation of the foveal avascular zone (FAZ) in optical coherence tomography (OCT) images obtained through the RedCheck® platform. Accurate FAZ segmentation is essential for ophthalmologic diagnoses in conditions such as diabetic retinopathy and age-related macular degeneration. The proposed method aims to overcome the limitation of labeled data, reducing both the cost and time associated with model training.

Methods

A total of 200 images from healthy patients were used. A neural network-based model was employed to identify the FAZ without specific labeled data, using pre-trained representations for contextual learning. Model performance was evaluated by comparing the automatic segmentation results with the manual annotations provided by specialists.

Results

Quantitative analysis revealed a mean intersection over union (MIoU) of 0.86, indicating consistent model performance in identifying regions of interest. The median IoU was 0.89, with an interquartile range between 0.85 (Q1) and 0.92 (Q3), demonstrating the method’s precision in most samples. Extreme values showed a maximum IOU of 0.97, reflecting excellent agreement, whereas the minimum IoU of 0.03 revealed limitations in atypical cases. The standard deviation of 0.11 indicated moderate variation in the results, and the 95% confidence interval for the MIoU ranged from 0.84 to 0.89, ensuring the statistical reliability of the approach.

Discussion

The findings demonstrate the feasibility and accuracy of the ZSL-based method for FAZ segmentation, even in the absence of labeled data. Despite the positive results, variability observed in specific images highlights the need for improvements to increase the model’s robustness in more heterogeneous data scenarios.