Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos

Michael Döllinger, Tobias Schraut, Lea A. Henrich, Dinesh Chhetri, Matthias Echternach, Aaron M. Johnson, Melda Kunduk, Youri Maryn, Rita R. Patel, Robin Samlan, Marion Semmler, Anne Schützenberger

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Endoscopic high-speed video (HSV) systems for visualization and assessment of vocal fold dynamics in the larynx are diverse and technically advancing. To consider resulting “concepts shifts” for neural network (NN)-based image processing, re-training of already trained and used NNs is necessary to allow for sufficiently accurate image processing for new recording modalities. We propose and discuss several re-training approaches for convolutional neural networks (CNN) being used for HSV image segmentation. Our baseline CNN was trained on the BAGLS data set (58,750 images). The new BAGLS-RT data set consists of additional 21,050 images from previously unused HSV systems, light sources, and different spatial resolutions. Results showed that increasing data diversity by means of preprocessing already improves the segmentation accuracy (mIoU + 6.35%). Subsequent re-training further increases segmentation performance (mIoU + 2.81%). For re-training, finetuning with dynamic knowledge distillation showed the most promising results. Data variety for training and additional re-training is a helpful tool to boost HSV image segmentation quality. However, when performing re-training, the phenomenon of catastrophic forgetting should be kept in mind, i.e., adaption to new data while forgetting already learned knowledge.

Original languageEnglish (US)
Article number9791
JournalApplied Sciences (Switzerland)
Issue number19
StatePublished - Oct 2022


  • catastrophic forgetting
  • concept shifts
  • convolutional neural networks
  • finetuning
  • glottis
  • high-speed imaging
  • medical image segmentation
  • re-training
  • voice

ASJC Scopus subject areas

  • General Materials Science
  • Instrumentation
  • General Engineering
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


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