BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation

Gómez P, Kist A, Schlegel P, Berry DA, Chhetri DK, Dürr S, Echternach M, Johnson AM, Kniesburges S, Kunduk M, Maryn Y, Schützenberger A, Verguts M, Döllinger M (2020)

Publication Type: Journal article

Publication year: 2020

Journal

Book Volume: 7

Article Number: 186

Journal Issue: 1

DOI: 10.1038/s41597-020-0526-3

Abstract

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.

Authors with CRIS profile

Related research project(s)

Involved external institutions

Klinikum der Universität München Germany (DE) University of California Los Angeles (UCLA) United States (USA) (US) New York University (NYU) United States (USA) (US) Louisiana State University United States (USA) (US) GZA Ziekenhuizen campus Sint-Augustinus Belgium (BE)

How to cite

APA:

Gómez, P., Kist, A., Schlegel, P., Berry, D.A., Chhetri, D.K., Dürr, S.,... Döllinger, M. (2020). BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation. Scientific Data, 7(1). https://dx.doi.org/10.1038/s41597-020-0526-3

MLA:

Gómez, Pablo, et al. "BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation." Scientific Data 7.1 (2020).

BibTeX: Download