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Related Experiment Video

Updated: Jan 9, 2026

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

Published on: July 5, 2024

Retinal vessel segmentation using multi scale feature attention with MobileNetV2 encoder.

Tanishq Soni¹, Sheifali Gupta², Salil Bharany²

¹Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, Punjab, India. tanishq.soni@chitkara.edu.in.

Scientific Reports

|December 9, 2025

Summary

This summary is machine-generated.

This study presents MSFAUMobileNet for retinal blood vessel segmentation, crucial for early disease detection. The model achieves high accuracy, aiding in diagnosing conditions like diabetic retinopathy and glaucoma.

Keywords:

Attention mechanism Diabetic retinopathy Glaucoma MobileNetV2 Multi-Scale feature aggregation (MSFA)Residual connections Retinal vessel segmentation U-Net architecture

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Last Updated: Jan 9, 2026

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Published on: November 30, 2022

Area of Science:

Medical Imaging
Computer Vision
Ophthalmology

Background:

Retinal blood vessel segmentation is vital for diagnosing diseases like diabetic retinopathy, glaucoma, and AMD.
Accurate segmentation aids in early detection and monitoring of retinal conditions.
Existing methods may struggle with the complexity of retinal vascular networks.

Purpose of the Study:

To introduce the MSFAUMobileNet model for enhanced retinal blood vessel segmentation.
To improve the accuracy and efficiency of retinal image analysis for disease detection.
To provide a computationally efficient tool for clinical practice.

Main Methods:

Developed MSFAUMobileNet, a U-Net architecture incorporating Multi-Scale Feature Aggregation (MSFA), residual connections, and attention mechanisms.
Employed MobileNetV2 as the encoder to extract hierarchical features from 13 bottleneck layers.
Integrated MSFA to capture spatial information across multiple resolutions for precise vascular network outlining.

Main Results:

The MSFAUMobileNet model achieved exceptional performance on the DRIVE dataset.
Achieved high segmentation accuracy (99.99%), Dice coefficient (99.95%), and Intersection over Union (IoU) (99.94%).
Demonstrated efficient separation of complex retinal vascular networks.

Conclusions:

MSFAUMobileNet significantly improves retinal blood vessel segmentation accuracy and efficiency.
The model's precision and speed make it suitable for medical image analysis in clinical settings.
Facilitates early diagnosis and management of retinal diseases through advanced image analysis.