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RSAP-Net: joint optic disc and cup segmentation with a residual spatial attention path module and MSRCR-PT

Yun Jiang1, Zeqi Ma2, Chao Wu1

  • 1Department of Computer Science and Engineering, Northwest Normal University, Lanzhou, China.

BMC Bioinformatics
|December 6, 2022
PubMed
Summary
This summary is machine-generated.

Accurate segmentation of the optic disc (OD) and optic cup (OC) is crucial for early glaucoma detection. RSAP-Net, a novel deep learning model, achieves high-accuracy joint OD and OC segmentation using a residual spatial attention path and advanced pre-processing.

Keywords:
Attention mechanismConvolutional neural workGlaucoma screeningJoint optic disc and cup segmentationPre-processingRetinex theory

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Area of Science:

  • Medical Imaging
  • Ophthalmology
  • Computer Vision

Background:

  • Glaucoma leads to irreversible blindness, necessitating early detection through accurate optic disc (OD) and optic cup (OC) segmentation.
  • Current convolutional neural network (CNN) methods often segment OD and OC separately, neglecting their inherent spatial relationship and limiting segmentation accuracy.
  • Early-stage glaucoma lacks symptoms, making automated segmentation of ocular structures vital for screening and prevention.

Purpose of the Study:

  • To propose a novel encoder-decoder segmentation framework, RSAP-Net, for the joint segmentation of the optic disc and optic cup.
  • To leverage the spatial relationship between OD and OC to improve segmentation performance.
  • To enhance the precision of OD and OC boundary delineation for better glaucoma screening.

Main Methods:

  • Developed RSAP-Net, an encoder-decoder network featuring a U-shaped backbone and a novel Residual Spatial Attention Path (RSAP) module.
  • Integrated a pre-processing technique, Multi-Scale Retinex Colour Recovery and Polar Transformation (MSRCR-PT), to refine image quality and feature extraction.
  • Employed a joint segmentation approach that considers the inherent spatial containment of the optic cup within the optic disc.

Main Results:

  • RSAP-Net demonstrated excellent segmentation performance on the Drishti-GS1 dataset.
  • Achieved high F1 scores of 0.9752 for OD segmentation and 0.9012 for OC segmentation.
  • Reported boundary location errors (BLE) of 6.33 pixels for OD and 11.97 pixels for OC, indicating precise localization.

Conclusions:

  • RSAP-Net provides an effective framework for the joint segmentation of optic discs and optic cups.
  • The proposed Residual Spatial Attention Path module and MSRCR-PT pre-processing significantly improve segmentation accuracy.
  • The method's validated effectiveness on the Drishti-GS1 dataset supports its utility in glaucoma screening and prevention.