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Updated: Jun 25, 2025

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

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

Published on: November 30, 2022

Quality-driven deep cross-supervised learning network for semi-supervised medical image segmentation.

Zhenxi Zhang¹, Heng Zhou², Xiaoran Shi¹

¹The Ministry of Education, Key Laboratory of Electronic Information Counter-measure and Simulation, Xidian University, Xi'an 710071, China; School of Electronic Engineering, Xidian University, Xi'an 710071, China.

Computers in Biology and Medicine

|May 21, 2024

Summary

This summary is machine-generated.

This study introduces Quality-driven Deep Cross-supervised Learning Network (QDC-Net) for efficient semi-supervised medical image segmentation. QDC-Net improves accuracy by managing sub-network disagreement and enhancing training reliability.

Keywords:

Cross-supervised learning Evidential learning Medical image segmentation Semi-supervised learning

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Area of Science:

Medical Image Analysis
Machine Learning
Computer Vision

Background:

Semi-supervised learning reduces annotation burden in medical image segmentation.
Existing cross-supervised methods struggle with sub-network disagreement and training efficiency.

Purpose of the Study:

Introduce a novel Quality-driven Deep Cross-supervised Learning Network (QDC-Net).
Address challenges in sub-network disagreement and training reliability for semi-supervised medical image segmentation.

Main Methods:

QDC-Net employs an evidential and a vanilla sub-network to manage disagreement.
Real-time quality estimation and directional cross-training with directional weights enhance reliability.
Truncated sample-wise loss weighting mitigates inaccurate predictions.

Main Results:

QDC-Net demonstrated superior performance in semi-supervised medical image segmentation.
Experiments on LA and Pancreas-CT datasets confirmed QDC-Net's effectiveness.
The proposed methods significantly improved segmentation accuracy and training efficiency.

Conclusions:

QDC-Net offers a robust and efficient solution for semi-supervised medical image segmentation.
The framework effectively handles sub-network disagreement and improves training reliability.
QDC-Net represents a significant advancement in automated medical image analysis.