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DPLSeg: unsupervised general segmentation model for retinal images across multiple OCT devices.

Caiye Fan1, Huankai Yu2,3,4, Zuoping Tan1

  • 1Wenzhou University of Technologyxs, Wenzhou, Zhejiang, China.

Biomedical Optics Express
|February 16, 2026
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Summary

This study introduces dual-level pseudo-label learning for segmentation (DPLSeg), an unsupervised model improving retinal-layer segmentation in optical coherence tomography images. DPLSeg enhances accuracy and domain adaptability, reducing annotation needs for early disease detection.

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Early detection of retinal diseases like myopic and diabetic retinopathy is crucial.
  • Accurate retinal-layer segmentation in optical coherence tomography (OCT) images is essential for diagnosis.
  • Current deep learning models struggle with domain shifts and high annotation costs.

Purpose of the Study:

  • To develop an unsupervised deep learning model for precise retinal-layer segmentation.
  • To improve model generalization across different OCT devices and reduce annotation dependency.
  • To enhance early detection of retinal diseases through improved OCT image analysis.

Main Methods:

  • Proposed dual-level pseudo-label learning for segmentation (DPLSeg).
  • Incorporated a hierarchical transformer encoder for enhanced feature representation and domain adaptability.
  • Utilized an unsupervised learning strategy to minimize annotation requirements.

Main Results:

  • DPLSeg achieved a mean intersection over union (mIoU) of 79.9% on 850 OCT images from three devices.
  • Outperformed existing models like DeepLab (75.2% mIoU) and DAFormer.
  • Reduced annotation needs by 80% compared to supervised methods.

Conclusions:

  • DPLSeg offers a robust and accurate solution for retinal-layer segmentation in OCT images.
  • The model demonstrates strong domain adaptability, addressing challenges posed by multi-device data.
  • DPLSeg presents a scalable and cost-effective tool for clinical diagnostics and early retinal disease detection.