Scholarlink

Objective <p>This study aims to develop a rapid and precise OCT segmentation model that simultaneously delineates retinal layers and fluid regions in age-related macular degeneration (AMD).</p> Methods <p>AMD-UPerNet was proposed as the segmentation model designed to simultaneously delineate retinal layers and fluid compartments in OCT images. Key innovations include: (1) Swin Transformer was adopted as the backbone to extract multi-scale features and model global contextual relationships; (2) Content-Aware ReAssembly of FEatures (CARAFE) module was introduced into feature pyramid network to improve up-sampling and mitigate feature loss; (3) The combination of cross-entropy and Dice loss was used to handle class imbalance and reduce mis-segmentation of fluid regions.</p> Result <p>Compared to the models such as PSPNet, DANet, OCNet, DenseASPP, SAM, MedSAM, and SegFormer, AMD-UPerNet achieved superior performance, with a Pixel Accuracy (PA) of 98.51%, Mean Pixel Accuracy (MPA) of 87.49%, Mean Precision (MPre) of 89.40%, and Mean Intersection over Union (MIoU) of 80.53%.</p> Conclusion <p>Unlike previous methods that segment retinal layers and fluid regions separately, our model uses a joint framework that enables simultaneous optimization and contextual interaction. By using the anatomical continuity of retinal layers as structural priors, AMD-UPerNet improves fluid localization and enhances robustness against ambiguous boundaries and low-contrast regions. These results highlight AMD-UPerNet’s potential to improve OCT segmentation accuracy and efficiency, facilitating early diagnosis and optimized treatment for AMD.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AMD-UPerNet: a tool for retinal layer and fluid assessment in age-related macular degeneration

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月21日

查看全文
  • Qian Ma,
  • Xuan Liu,
  • Jing Li,
  • Yun Bai,
  • Wan Mu,
  • Na Li,
  • Biao Yan,
  • Zhenhua Wang

摘要

Objective

This study aims to develop a rapid and precise OCT segmentation model that simultaneously delineates retinal layers and fluid regions in age-related macular degeneration (AMD).

Methods

AMD-UPerNet was proposed as the segmentation model designed to simultaneously delineate retinal layers and fluid compartments in OCT images. Key innovations include: (1) Swin Transformer was adopted as the backbone to extract multi-scale features and model global contextual relationships; (2) Content-Aware ReAssembly of FEatures (CARAFE) module was introduced into feature pyramid network to improve up-sampling and mitigate feature loss; (3) The combination of cross-entropy and Dice loss was used to handle class imbalance and reduce mis-segmentation of fluid regions.

Result

Compared to the models such as PSPNet, DANet, OCNet, DenseASPP, SAM, MedSAM, and SegFormer, AMD-UPerNet achieved superior performance, with a Pixel Accuracy (PA) of 98.51%, Mean Pixel Accuracy (MPA) of 87.49%, Mean Precision (MPre) of 89.40%, and Mean Intersection over Union (MIoU) of 80.53%.

Conclusion

Unlike previous methods that segment retinal layers and fluid regions separately, our model uses a joint framework that enables simultaneous optimization and contextual interaction. By using the anatomical continuity of retinal layers as structural priors, AMD-UPerNet improves fluid localization and enhances robustness against ambiguous boundaries and low-contrast regions. These results highlight AMD-UPerNet’s potential to improve OCT segmentation accuracy and efficiency, facilitating early diagnosis and optimized treatment for AMD.