A single latent channel is sufficient for biomedical glottis segmentation.

ABSTRACT: Glottis segmentation is a crucial step to quantify endoscopic footage in laryngeal high-speed videoendoscopy. Recent advances in deep neural networks for glottis segmentation allow for a fully automatic workflow. However, exact knowledge of integral parts of these deep segmentation networks remains unknown, and understanding the inner workings is crucial for acceptance in clinical practice. Here, we show that a single latent channel as a bottleneck layer is sufficient for glottal area segmentation using systematic ablations. We further demonstrate that the latent space is an abstraction of the glottal area segmentation relying on three spatially defined pixel subtypes allowing for a transparent interpretation. We further provide evidence that the latent space is highly correlated with the

SUBMITTER: Kist AM 

PROVIDER: S-EPMC9395348 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature