Predicting semantic segmentation quality in laryngeal endoscopy images

Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to asses...

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Vydáno v:PloS one Ročník 20; číslo 7; s. e0314573
Hlavní autoři: Kist, Andreas M., Razi, Sina, Groh, René, Gritsch, Florian, Schützenberger, Anne
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States Public Library of Science 03.07.2025
Public Library of Science (PLoS)
Témata:
Algorithms
Analysis
Artificial Intelligence
Biology and Life Sciences
Computer and Information Sciences
Datasets
Deep learning
Endoscopy
Engineering and Technology
Humans
Image processing
Image Processing, Computer-Assisted - methods
Image quality
Image segmentation
Laryngoscopy
Laryngoscopy - methods
Larynx - diagnostic imaging
Masks
Medical imaging
Medicine and Health Sciences
Methods
Neural networks
Physiology
Pixels
Semantic segmentation
Semantics
Social Sciences
Subject specialists
Traffic signals
Germany
ISSN:1932-6203, 1932-6203
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Popis
Shrnutí:Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is for example used to detect cancer tissue or to assess laryngeal physiology. However, due to the diversity of patients presenting, it is necessary to judge the segmentation quality. In this study, we present a fully automatic system to evaluate the segmentation performance in laryngeal endoscopy images. We showcase on glottal area segmentation that the predicted segmentation quality represented by the intersection over union metric is on par with human raters. Using a traffic light system, we are able to identify problematic segmentation frames to allow human-in-the-loop improvements, important for the clinical adaptation of automatic analysis procedures.
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Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0314573