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Related Experiment Video

Updated: May 11, 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

Integrating orthogonal supervision for sparse semi-supervised 3D medical image segmentation.

Suruchi Kumari1, Pravendra Singh1

  • 1Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India.

Neural Networks : the Official Journal of the International Neural Network Society
|May 9, 2026
PubMed
Summary

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Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives.

Computers in biology and medicine·2024

Integrating Orthogonal Supervision (IOS) enhances 3D medical image segmentation using sparsely annotated data. This novel approach leverages all orthogonal views simultaneously for more accurate and holistic learning, outperforming existing methods.

Area of Science:

  • Medical Imaging
  • Computer Vision
  • Machine Learning

Background:

  • Semi-supervised learning (SSL) advances 3D medical image segmentation.
  • Fully annotating volumetric data is time-consuming and costly.
  • Sparsely annotated SSL uses limited labeled data but often yields suboptimal performance due to view-specific training.

Purpose of the Study:

  • To develop a more effective semi-supervised learning framework for 3D medical image segmentation using sparse annotations.
  • To address the limitations of view-specific training in current sparsely annotated SSL methods.
  • To propose a novel strategy that integrates supervisory signals from all orthogonal views.

Main Methods:

  • Proposed Integrating Orthogonal Supervision (IOS), a strategy using a single 3D encoder to learn from axial, sagittal, and coronal planes.
Keywords:
Deep learningMedical image segmentationOrthogonal supervisionSemi-supervised learningSparse semi-supervised learning

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Last Updated: May 11, 2026

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  • Introduced a Tri-Decoder Framework for learning from both sparsely labeled and unlabeled data.
  • Implemented a cross-decoder supervision strategy for unlabeled slices within labeled volumes and used joint pseudo-labels for unlabeled volumes.

    • Main Results:

    • The IOS framework demonstrated superior performance compared to state-of-the-art methods on LA, Pancreas, and KiTS19 datasets under sparse supervision.
    • Simultaneous integration of orthogonal views led to a richer understanding of volumetric data distributions.
    • The proposed Tri-Decoder Framework and cross-decoder supervision improved the quality of pseudo-labels and overall segmentation accuracy.

    Conclusions:

    • Integrating orthogonal views within a single framework is more effective for sparsely annotated 3D medical image segmentation.
    • The proposed IOS strategy with its Tri-Decoder Framework offers a robust solution for leveraging limited annotations.
    • This approach significantly improves 3D medical image segmentation accuracy, offering a more efficient alternative to full annotation.