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Updated: Oct 17, 2025

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NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation.

Samiksha Pachade1, Prasanna Porwal1, Manesh Kokare1

  • 1Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.

Medical Image Analysis
|October 6, 2021
PubMed
Summary
This summary is machine-generated.

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A new deep learning model, Nested EfficientNet (NENet), accurately segments optic disc and optic cup for glaucoma screening. This automated method shows potential for early detection of irreversible vision loss.

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a leading cause of irreversible vision loss.
  • Accurate optic disc (OD) and optic cup (OC) segmentation is crucial for glaucoma screening.
  • Deep learning has shown promise in improving OD and OC segmentation.

Purpose of the Study:

  • To introduce a novel deep learning network, Nested EfficientNet (NENet), for enhanced OD and OC segmentation.
  • To evaluate the performance and generalizability of NENet on public glaucoma datasets.

Main Methods:

  • NENet utilizes EfficientNetB4 as an encoder with nested residual blocks, ASPP, and attention gates.
  • A combination of cross-entropy and dice coefficient loss was employed for training.
  • A modified patch-based discriminator was incorporated to refine local segmentation details.
Keywords:
Adversarial learningDeep learningEfficientnetGlaucomaOptic cup segmentationOptic disc segmentation

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  • The network was evaluated on the REFUGE, Drishti-GS, and RIM-ONE-r3 datasets.

    • Main Results:

    • NENet achieved superior performance compared to state-of-the-art methods in OD and OC segmentation.
    • The proposed network demonstrated excellent generalizability across different camera types and image resolutions.
    • NENet effectively improved local segmentation details.

    Conclusions:

    • The developed NENet model offers a robust and accurate solution for automated glaucoma screening.
    • The technique shows significant potential as a key component in automated glaucoma detection systems.