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Related Experiment Video

Updated: Apr 9, 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

3.7K

OOD-SEG: Exploiting out-of-distribution detection techniques for learning image segmentation from sparse multi-class

Junwen Wang1, Zhonghao Wang1, Oscar MacCormac2

  • 1School of Biomedical Engineering & Imaging Sciences, King's College London, UK.

Medical Image Analysis
|April 7, 2026
PubMed
Summary

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This study introduces a new deep learning method for medical image segmentation using sparse positive-only labels and out-of-distribution detection. It efficiently segments images without needing background annotations, improving accuracy and reliability.

Area of Science:

  • Computer Vision
  • Medical Imaging
  • Machine Learning

Background:

  • Medical image segmentation using deep neural networks faces challenges with time-consuming, expert-required pixel-level annotations.
  • Existing segmentation models struggle to detect out-of-distribution (OOD) pixels, leading to unreliable outputs in real-world applications.

Purpose of the Study:

  • To develop a novel segmentation approach addressing the limitations of sparse annotations and OOD detection in medical imaging.
  • To formulate multi-class segmentation with sparse positive-only annotations as a pixel-wise positive-unlabelled (PU) learning problem, leveraging OOD detection techniques.

Main Methods:

  • Proposed a framework utilizing positive-unlabelled (PU) learning and OOD detection techniques for segmentation.
  • Learned from sparsely annotated pixels of multiple positive-only classes, excluding background annotations, treating them as part of the OOD set.
Keywords:
Hyperspectral imagingOne-class classificationOut-of-distribution detectionPositive-unlabelled learningSemantic segmentationWeakly supervised learning

Related Experiment Videos

Last Updated: Apr 9, 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

3.7K
  • Integrated pixel-level OOD detection approaches and proposed a cross-validation strategy treating held-out classes as OOD.

    • Main Results:

    • Demonstrated the framework's ability to segment medical images using only sparse positive-only annotations.
    • Showcased the integration of OOD detection for enhanced segmentation reliability.
    • Validated the robustness and generalization capability through extensive experiments on hyperspectral and RGB surgical imaging datasets.

    Conclusions:

    • The proposed framework offers an effective solution for medical image segmentation with limited annotations.
    • Leveraging OOD detection techniques enhances the robustness of segmentation models.
    • The approach shows significant promise for improving segmentation accuracy and reliability in medical and surgical imaging.