Re-Training of Convolutional Neural Networks for Glottis Segmentation in Endoscopic High-Speed Videos.

ABSTRACT: Endoscopic high-speed video (HSV) systems for visualization and assessment of vocal fold dynamics in the larynx are diverse and technically advancing. To consider resulting "concepts shifts" for neural network (NN)-based image processing, re-training of already trained and used NNs is necessary to allow for sufficiently accurate image processing for new recording modalities. We propose and discuss several re-training approaches for convolutional neural networks (CNN) being used for HSV image segmentation. Our baseline CNN was trained on the BAGLS data set (58,750 images). The new BAGLS-RT data set consists of additional 21,050 images from previously unused HSV systems, light sources, and different spatial resolutions. Results showed that increasing data diversity by means of preprocessing

SUBMITTER: Dollinger M 

PROVIDER: S-EPMC10427138 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature