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Multi-class retinal fluid joint segmentation based on cascaded convolutional neural networks.

Wei Tang1, Yanqing Ye1, Xinjian Chen2

  • 1MIPAV Lab, School of Electronic and Information Engineering, Soochow University, Jiangsu 215006, People's Republic of China.

Physics in Medicine and Biology
|May 25, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new deep learning framework for segmenting multiple types of retinal fluid in optical coherence tomography (OCT) images. The method accurately identifies intra-retinal fluid, sub-retinal fluid, and pigment epithelial detachment, aiding in diagnosing eye diseases.

Keywords:
convolutional neural networkmedical image segmentationoptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Accurate segmentation of retinal fluid (intra-retinal fluid, sub-retinal fluid, pigment epithelial detachment) in OCT images is crucial for diagnosing and treating fundus diseases.
  • Existing methods may face challenges in precisely delineating these fluid types.

Purpose of the Study:

  • To develop and evaluate a novel two-stage, multi-class joint segmentation framework for retinal fluid in OCT images.
  • To improve the accuracy and efficiency of segmenting intra-retinal fluid, sub-retinal fluid, and pigment epithelial detachment.

Main Methods:

  • A cascaded convolutional neural network framework was proposed, featuring a U-shape encoder-decoder for initial segmentation and a specialized ICAF-Net for joint fluid segmentation.
  • The framework incorporates spatial prior information and enhanced attention mechanisms for improved segmentation performance.

Main Results:

  • The proposed framework achieved an average Dice similarity coefficient of 76.39%, intersection over union of 64.03%, and accuracy of 99.32% on the RETOUCH challenge dataset.
  • The method demonstrated strong performance in the joint segmentation of multiple retinal fluid types.

Conclusions:

  • The developed framework offers a robust solution for the joint segmentation of intra-retinal fluid, sub-retinal fluid, and pigment epithelial detachment in OCT images.
  • The proposed method outperforms several state-of-the-art segmentation networks, highlighting its clinical potential.