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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

TransDualSegNet: transformer dual segment network for retinal vasculature segmentation in OCT.

Ligang Jiang¹, Jing Hu^2,3, Zhuoran Wang^2,3

¹Department of Ophthalmology, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China.

Quantitative Imaging in Medicine and Surgery

|October 13, 2025

Summary

This study introduces TransDualSegNet, an efficient AI model for segmenting retinal vessels in OCT images. It achieves high accuracy, aiding in early diagnosis of eye diseases like diabetic retinopathy.

Keywords:

Ophthalmic medical image processing artificial intelligence deep learning optical coherence tomography (OCT)ultra-fine semantic segmentation

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Published on: October 2, 2021

Area of Science:

Ophthalmology
Medical Imaging
Artificial Intelligence

Background:

Early diagnosis and treatment of eye diseases are vital for vision preservation.
Optical coherence tomography (OCT) is a key non-invasive imaging technique in ophthalmology.
Retinal vessel segmentation in OCT images provides crucial diagnostic information.

Purpose of the Study:

To develop an efficient and accurate method for retinal vessel segmentation in OCT images.
To improve the diagnosis and treatment of eye diseases through enhanced image analysis.

Main Methods:

Developed Transformer Dual Segment Network (TransDualSegNet), an encoder-decoder model using Pyramid Vision Transformer.
Incorporated a dual-branch convolutional network in the decoder to emphasize edge information.
Utilized a feature fusion module (FFM) to combine global and edge features for segmentation.

Main Results:

TransDualSegNet outperformed U-Net, Attention U-Net, and Trans U-Net in accuracy (91.13%), Jaccard similarity (93.92%), and Dice coefficient (90.26%).
Achieved a fast forward inference time of 20.3 ms, balancing high accuracy and efficiency.
Ablation studies confirmed the effectiveness of each component, with IOU reaching 93.92% and AUC 93.04%.

Conclusions:

TransDualSegNet offers a novel and high-performing solution for retinal vessel segmentation in OCT images.
The model's technical advantages and clinical potential can advance medical image processing and ophthalmic diagnostics.
Accurate segmentation aids in diagnosing conditions like diabetic retinopathy and hypertensive retinopathy, improving treatment recommendations.