Cargando…
BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation
Laryngeal videoendoscopy is one of the main tools in clinical examinations for voice disorders and voice research. Using high-speed videoendoscopy, it is possible to fully capture the vocal fold oscillations, however, processing the recordings typically involves a time-consuming segmentation of the...
Autores principales: | Gómez, Pablo, Kist, Andreas M., Schlegel, Patrick, Berry, David A., Chhetri, Dinesh K., Dürr, Stephan, Echternach, Matthias, Johnson, Aaron M., Kniesburges, Stefan, Kunduk, Melda, Maryn, Youri, Schützenberger, Anne, Verguts, Monique, Döllinger, Michael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7305104/ https://www.ncbi.nlm.nih.gov/pubmed/32561845 http://dx.doi.org/10.1038/s41597-020-0526-3 |
Ejemplares similares
-
Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos
por: Döllinger, Michael, et al.
Publicado: (2022) -
Interdependencies between acoustic and high-speed videoendoscopy parameters
por: Schlegel, Patrick, et al.
Publicado: (2021) -
Long-term performance assessment of fully automatic biomedical glottis segmentation at the point of care
por: Groh, René, et al.
Publicado: (2022) -
A single latent channel is sufficient for biomedical glottis segmentation
por: Kist, Andreas M., et al.
Publicado: (2022) -
Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings
por: Schlegel, Patrick, et al.
Publicado: (2020)