Predicting semantic segmentation quality in laryngeal endoscopy images

• Published in: PloS one Vol. 20; no. 7; p. e0314573
• Main Authors: Kist, Andreas M., Razi, Sina, Groh, René, Gritsch, Florian, Schützenberger, Anne
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 03.07.2025; Public Library of Science (PLoS)
• Subjects: 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

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...