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Updated: Jun 13, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Query-Driven Retinal Layer Segmentation in OCT Using Cross-Attentive Feature Learning.

Nebras Sobahi1, Salih Taha Alperen Özçelik2, Orhan Atila3

  • 1Department of Electrical and Electronics Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|June 12, 2026
PubMed
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This summary is machine-generated.

RetiQueryNet, a novel query-based deep learning model, enhances retinal layer segmentation in OCT scans by improving anatomical consistency and accuracy, outperforming existing methods for diagnosing eye diseases.

Area of Science:

  • Medical Imaging
  • Deep Learning
  • Ophthalmology

Background:

  • Accurate retinal layer segmentation in OCT is crucial for diagnosing and monitoring retinal diseases like AMD and DME.
  • Existing deep learning methods often lack anatomical consistency, especially in challenging regions.
  • This study introduces a query-based framework to address these limitations.

Purpose of the Study:

  • To develop and evaluate RetiQueryNet, a query-based segmentation framework for improved retinal layer analysis.
  • To enhance anatomical consistency in OCT segmentation, particularly in areas with low contrast or deformation.
  • To outperform current state-of-the-art models in retinal layer segmentation accuracy.

Main Methods:

  • Proposed the RetiQueryNet architecture using query embeddings and cross-attention with a transformer encoder.
Keywords:
OCTcross-attentionmedical image segmentationquery-based learningretinal layer segmentationtransformer

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  • Integrated multi-scale features via a compact query-driven decoder.
  • Trained the model using a combination of cross-entropy and Dice loss, comparing performance against U-Net, DeepLabV3, FPN, MANet, and SegFormer.

    • Main Results:

    • RetiQueryNet achieved a mean Dice score of 0.934 ± 0.0046, outperforming all baseline models.
    • Significant improvements were observed in challenging layers like IBRPE and OBRPE.
    • The model demonstrated lower mean surface distance (MSD), indicating more accurate boundary predictions and coherent segmentations.

    Conclusions:

    • Query-based modeling is a viable approach for accurate and anatomically consistent pixel-wise segmentation in medical imaging.
    • RetiQueryNet leverages structural priors through learnable queries to enhance segmentation accuracy and consistency.
    • Query-based methods show promise for retinal image segmentation and other medical imaging applications.