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Related Experiment Video

Updated: Nov 4, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Semi-Automated Machine Learning Video Annotation for Gastroenterologists.

Adrian Krenzer1, Kevin Makowski1, Amar Hekalo1

  • 1Julius-Maximilian University of Würzburg, Germany.

Studies in Health Technology and Informatics
|May 27, 2021
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A new semi-automatic tool speeds up endoscopic video annotation by nearly twofold. Utilizing trained object detection models, this novel workflow enhances efficiency in medical video analysis.

Keywords:
deep learningendoscopymachine learningvideo annotation tool

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Area of Science:

  • Medical imaging and computer vision
  • Endoscopic video analysis
  • Artificial intelligence in healthcare

Background:

  • Accurate annotation of endoscopic videos is crucial for diagnosis and training.
  • Current annotation methods can be time-consuming and labor-intensive.
  • Object detection models offer potential for automating parts of the annotation process.

Purpose of the Study:

  • To present a semi-automatic tool for efficient endoscopic video annotation.
  • To implement a novel workflow for improved annotation speed and accuracy.
  • To evaluate the performance of the tool against existing methods.

Main Methods:

  • Development of a semi-automatic annotation tool.
  • Integration of trained object detection models.
  • Implementation of a novel annotation workflow.
  • Comparative analysis of annotation time and accuracy.

Main Results:

  • The novel tool demonstrates significantly faster annotation times.
  • Preliminary results indicate the annotation process is nearly twice as fast.
  • The tool maintains accuracy comparable to current state-of-the-art methods.

Conclusions:

  • The developed semi-automatic tool offers a substantial improvement in endoscopic video annotation efficiency.
  • The novel workflow streamlines the annotation process, saving valuable time for medical professionals.
  • This technology has the potential to advance medical video analysis and training.