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Deep Learning-Based Detection of Glottis Segmentation Failures.

Armin A Dadras1, Philipp Aichinger1

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Summary

This study introduces a novel deep learning method to automatically detect inaccurate glottis segmentations in medical images. The approach achieves high accuracy, reducing manual labor in voice research and diagnostics.

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computer visiondeep learningfailure detectionglottis segmentationhigh-speed videolaryngoscopysemantic segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Accurate medical image segmentation is essential for clinical applications, yet noise and variability pose significant challenges.
  • Precise glottis segmentation from high-speed videos is critical for voice research and diagnostics.
  • Manual identification of segmentation failures is time-consuming and inefficient.

Purpose of the Study:

  • To develop and evaluate the first deep learning framework for automated detection of faulty glottis segmentations.
  • To improve the efficiency and reliability of glottis segmentation analysis in medical imaging.
  • To establish a robust method for identifying suboptimal segmentation outcomes.

Main Methods:

  • Generated faulty glottis segmentations using a poorly performing neural network and a novel knowledge-driven perturbation procedure on public datasets.
  • Applied extensive data augmentation and image transformations to create diverse failure cases.
  • Trained a ResNet18 neural network with custom loss functions to predict segmentation quality scores (IoU).

Main Results:

  • The proposed deep learning model achieved 88.27% classification accuracy in detecting faulty segmentations.
  • The system demonstrated high specificity (91.54%) in identifying incorrect segmentations.
  • The method effectively predicts Intersection over Union (IoU) scores, enabling classification via a fixed threshold.

Conclusions:

  • The developed deep learning approach offers an effective and automated solution for detecting faulty glottis segmentations.
  • This framework significantly reduces the need for manual review, streamlining voice research and diagnostic processes.
  • The study highlights the potential of AI in enhancing the quality control of medical image segmentation.