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Related Experiment Video

Updated: Jun 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

Annotation-efficient medical image segmentation via cross-latent graphs and vector-quantized memory.

Yanyu Xu1, Menghan Zhou2, Xinxing Xu3

  • 1Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Jinan, China.

Medical Image Analysis
|June 26, 2026
PubMed
Summary

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This summary is machine-generated.

This study introduces a new framework for efficient medical image segmentation using minimal annotations like points and scribbles. It significantly reduces annotation costs while maintaining high segmentation accuracy, aiding computer-assisted diagnosis.

Area of Science:

  • Medical Imaging
  • Computer-Assisted Diagnosis
  • Machine Learning

Background:

  • Medical image segmentation is crucial for diagnosis but hindered by extensive annotation requirements.
  • Current methods struggle with scalability due to the need for large, pixel-level annotated datasets.

Purpose of the Study:

  • To develop an annotation-efficient framework for medical image segmentation.
  • To leverage sparse supervision (scribbles, points) to reduce annotation burden.
  • To achieve high segmentation accuracy comparable to fully supervised methods.

Main Methods:

  • Proposed a framework utilizing sparse supervision for medical image segmentation.
  • Employed an auxiliary reconstruction branch for enhanced supervision and feature enrichment.
Keywords:
Annotation-efficient learningMedical imaging segmentationPseudo label generationVector quantization (VQ)

Related Experiment Videos

Last Updated: Jun 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

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

Published on: November 30, 2022

  • Integrated a vector quantization (VQ) memory bank for dynamic pseudo-label generation.
  • Utilized a cross-latent graph neural network (GNN) to capture non-local dependencies and improve predictions.

    • Main Results:

    • Achieved competitive or superior performance against state-of-the-art weakly supervised methods on benchmark datasets (ACDC, BraTS'19, Pancreas-CT).
    • Demonstrated segmentation quality approaching fully supervised accuracy.
    • Showcased significant reduction in annotation costs.

    Conclusions:

    • The proposed framework effectively reduces annotation costs for medical image segmentation.
    • It offers a scalable solution for clinical applications by minimizing reliance on pixel-level annotations.
    • The approach maintains high segmentation quality, supporting reliable computer-assisted diagnosis.