BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation

• Published in: Scientific data Vol. 7; no. 1; p. 186
• Main Authors: 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
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 19.06.2020; Nature Publishing Group
• Subjects: 692/308; 692/698; Data Descriptor; Deep learning; Endoscopy; Glottis - diagnostic imaging; Glottis - physiology; Humanities and Social Sciences; Humans; Image processing; multidisciplinary; Neural networks; Oscillations; Researchers; Science; Science (multidisciplinary); Segmentation; Vocal Cords - diagnostic imaging; Vocal Cords - physiology; Voice Disorders - diagnosis
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-020-0526-3

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 glottal area by trained experts. Even though automatic methods have been proposed and the task is particularly suited for deep learning methods, there are no public datasets and benchmarks available to compare methods and to allow training of generalizing deep learning models. In an international collaboration of researchers from seven institutions from the EU and USA, we have created BAGLS, a large, multihospital dataset of 59,250 high-speed videoendoscopy frames with individually annotated segmentation masks. The frames are based on 640 recordings of healthy and disordered subjects that were recorded with varying technical equipment by numerous clinicians. The BAGLS dataset will allow an objective comparison of glottis segmentation methods and will enable interested researchers to train their own models and compare their methods. Measurement(s) glottis • Image Segmentation Technology Type(s) Endoscopic Procedure • neural network model Factor Type(s) age • sex • healthy versus disordered subjects • recording conditions Sample Characteristic - Organism Homo sapiens Machine-accessible metadata file describing the reported data: https://doi.org/10.6084/m9.figshare.12387890