BAGLS-VF: a comprehensive dataset for glottal area and vocal fold segmentations.

OBJECTIVE: Datasets for automatic analysis of laryngeal endoscopic images are scarce, particularly those providing detailed anatomical annotations beyond the glottal area. The BAGLS dataset previously introduced a large collection of laryngoscopic images with expert annotations of the glottal area. To facilitate research on more detailed anatomical modeling and segmentation tasks, we introduce BAGLS-VF, an extension of the BAGLS dataset that includes additional annotations of the left and right vocal folds. DATA DESCRIPTION: BAGLS-VF contains 59,250 videolaryngoscopic frames with pixel-level annotations of three anatomical structures: glottal area, left vocal fold, and right vocal fold. The dataset is derived from the original BAGLS dataset and was semi-automatically annotated by a trained rater using a standardized annotation protocol. Images originate from high-speed videolaryngoscopic recordings of subjects undergoing clinical examination and cover a wide range of anatomical variability and imaging conditions. In line with the original BAGLS dataset, the images are predominantly grayscale (approximately 97% of the underlying recordings) with a small proportion of RGB recordings (approximately 3%); the segmentation masks are provided as RGB-encoded label images in which the three anatomical regions are stored as separate colour channels. BAGLS-VF is intended to support research in medical image segmentation, laryngeal biomechanics, and computer-assisted diagnosis.