Digital Kymography for the Analysis of the Opening and Closure Intervals of Heart Valves

Friedl, Sven; König, S.; Kondruweit, M.; Wittenberg, Thomas

doi:10.1007/978-3-642-19335-4_31

Cited by 3 publications

(1 citation statement)

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“…We further provide the entire raw data used to create the BAGLS dataset. With this, algorithms based on time variant data can be trained and analyzed as suggested by 17 or used in studies focusing on kymography 53,54 .…”

Section: Videoendoscopy and Glottis Segmentation Several Imaging Tecmentioning

confidence: 99%

BAGLS, a multihospital Benchmark for Automatic Glottis Segmentation

et al. 2020

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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.

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Section: Videoendoscopy and Glottis Segmentation Several Imaging Tecmentioning

confidence: 99%