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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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ERANet: Edge replacement augmentation for semi-supervised meniscus segmentation with prototype consistency alignment

Siyue Li¹, Yongcheng Yao², Junru Zhong¹

¹CU Lab for AI in Radiology (CLAIR), Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China.

Neural Networks : the Official Journal of the International Neural Network Society

|November 21, 2025

Summary

This summary is machine-generated.

ERANet, a novel semi-supervised framework, improves meniscus segmentation in knee MRI using anatomically guided augmentation and iterative refinement. This approach enhances early knee osteoarthritis diagnosis by accurately identifying meniscal structures with limited labeled data.

Keywords:

Data augmentation Magnetic resonance imaging Meniscus segmentation Prototype consistency learning Semi-supervised learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Orthopedics

Background:

The meniscus is vital for knee stability and preventing osteoarthritis (OA).
Accurate meniscus segmentation in MRI is crucial for OA diagnosis and monitoring.
Manual segmentation is time-consuming; automatic methods struggle with anatomical variability and image quality.

Purpose of the Study:

To develop ERANet, a semi-supervised framework for robust meniscus segmentation in knee MRI.
To leverage both labeled and unlabeled data for improved segmentation accuracy.
To address challenges like morphological variability and low contrast in meniscus imaging.

Main Methods:

ERANet utilizes a semi-supervised approach with anatomically guided augmentation, consistency regularization, and pseudo-label refinement.
Key components include edge replacement augmentation (ERA), prototype consistency alignment (PCA), and conditional self-training (CST).
The framework integrates meniscus-specific augmentation (ERA) with generalizable learning modules (PCA, CST).

Main Results:

ERANet demonstrated superior segmentation performance on 3D DESS and 3D FSE MRI sequences compared to existing semi-supervised methods.
The framework achieved high accuracy even with minimal labeled data.
Ablation studies confirmed the effectiveness of individual components (ERA, PCA, CST) and their synergistic combination.

Conclusions:

ERANet provides a robust and scalable solution for meniscus segmentation in knee MRI.
The proposed framework effectively handles small, low-contrast anatomical structures with limited supervision.
ERANet facilitates improved early diagnosis and monitoring of knee osteoarthritis.