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Related Experiment Video

Updated: Feb 23, 2026

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TriFTM-Net: Tri-Path Fourier-Temporal Modulation Network for macular edema pathology segmentation and reconstruction

Xingru Huang1, Shuaibin Chen2, Huawei Wang2

  • 1Hangzhou Dianzi University, Hangzhou, 310018, Zhejiang, China; School of Electronic Engineering and Computer Science, Queen Mary University of London, London, E1 4NS, United Kingdom.

Neural Networks : the Official Journal of the International Neural Network Society
|February 21, 2026
PubMed
Summary

A new network, Tri-Path Fourier-Temporal Modulation Network (TriFTM-Net), enhances 3D retinal imaging for ophthalmic diseases. It improves accuracy in segmenting macular edema and retinal tears, aiding surgical outcomes.

Keywords:
Fourier transformMacular edemaMacular holeOCT SegmentationWavelet transform

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Computer Vision

Background:

  • Ophthalmic diseases significantly impact global vision.
  • Accurate 3D reconstruction of retinal lesions is vital for surgical success.
  • Existing 2D segmentation methods struggle with noisy and heterogeneous retinal image data.

Purpose of the Study:

  • To develop an advanced deep learning model for precise 3D retinal image segmentation.
  • To address challenges of noise and heterogeneity in macular edema and retinal tear imaging.
  • To improve the accuracy and efficiency of ophthalmic surgical planning and execution.

Main Methods:

  • Proposed the Tri-Path Fourier-Temporal Modulation Network (TriFTM-Net), integrating spatial, frequency, and spatiotemporal features.
  • Introduced three key modules: Tri-Path Spectral Hierarchical Encoder (TPSHE), Feature Re-Modulation (FRM), and Hierarchical Feature Reconstruction Module (HFRM).
  • Evaluated performance on the OIMHS dataset against thirteen baseline methods.

Main Results:

  • TriFTM-Net achieved superior performance compared to all thirteen baseline methods.
  • The network demonstrated the highest Dice scores, Intersection over Union (IoU), and Kappa coefficients.
  • The proposed architecture effectively handles noise and enhances feature representation for improved segmentation accuracy.

Conclusions:

  • TriFTM-Net offers a significant advancement in 3D retinal image segmentation for ophthalmic diseases.
  • The synergistic integration of features and specialized modules overcomes limitations of previous methods.
  • This approach holds promise for enhancing surgical planning and patient outcomes in ophthalmology.