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

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Uncertainty-Aware Cross-Training for Semi-Supervised Medical Image Segmentation.

Kaiwen Huang, Tao Zhou, Huazhu Fu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|August 29, 2025

Summary

This study introduces an Uncertainty-aware Cross-training framework for semi-supervised medical image Segmentation (UC-Seg). UC-Seg enhances segmentation accuracy by mitigating model biases and generating high-confidence pseudo-labels using uncertainty maps.

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Area of Science:

Medical Image Analysis
Machine Learning
Computer Vision

Background:

Semi-supervised learning reduces annotation needs in medical image segmentation.
Mean-teacher models leverage unlabeled data but can suffer from cognitive biases.
Generating high-confidence pseudo-labels from perturbed inputs is challenging.

Purpose of the Study:

To propose an Uncertainty-aware Cross-training framework for semi-supervised medical image Segmentation (UC-Seg).
To mitigate cognitive biases in models by using two distinct subnets.
To improve the generation of high-confidence pseudo-labels for enhanced segmentation.

Main Methods:

Developed the UC-Seg framework with two distinct subnets.
Implemented a Cross-subnet Consistency Preservation (CCP) strategy for feature consistency.
Introduced an Uncertainty-aware Pseudo-label Generation (UPG) component using segmentation results and uncertainty maps.

Main Results:

UC-Seg demonstrated superior segmentation accuracy across various medical imaging modalities (MRI, CT, ultrasound, colonoscopy).
The method achieved better generalization performance compared to existing state-of-the-art semi-supervised techniques.
The framework effectively mitigated model biases and improved pseudo-label quality.

Conclusions:

UC-Seg offers a robust approach to semi-supervised medical image segmentation.
The proposed CCP and UPG strategies enhance model performance and reliability.
The framework shows significant potential for clinical applications requiring accurate image segmentation.