UAAL Dataset: Upper Airway Anatomical Landmark Dataset for Automated Bronchoscopy and Intubation

Hao, Ruoyi; Zhang, Yang; Tang, Zhiqing; Zhou, Yang; Seenivasan, Lalithkumar; Chan, Catherine Po Ling; Chan, Jason Ying Kuen; Xu, Shuhui; Teo, Neville Wei Yang; Tay, Kaijun; Tan, Vanessa Yee Jueen; Thong, Jiun Fong; Kiong, Kimberley Liqin; Loh, Shaun; Toh, Song Tar; Lim, Chwee Ming; Ren, Hongliang

doi:10.6084/m9.figshare.26342779.v3

UAAL Dataset: Upper Airway Anatomical Landmark Dataset for Automated Bronchoscopy and Intubation

journal contribution

posted on 2024-10-17, 20:02 authored by Ruoyi HaoRuoyi Hao, Yang Zhang, Zhiqing Tang, Yang Zhou, Lalithkumar Seenivasan, Catherine Po Ling Chan, Jason Ying Kuen Chan, Shuhui Xu, Neville Wei Yang Teo, Kaijun Tay, Vanessa Yee Jueen Tan, Jiun Fong Thong, Kimberley Liqin Kiong, Shaun Loh, Song Tar Toh, Chwee Ming Lim, Hongliang Ren

The Upper Airway Anatomical Landmark (UAAL) Dataset is a comprehensive resource for advancing endoscopic navigation in automated bronchoscopy and intubation systems. It comprises 3,814 clinical bronchoscopy images with 10,325 annotations across 8 classes, complemented by 2,746 phantom images with 4,526 annotations across 9 classes. Covering the nasal cavity to the trachea, this dataset offers diverse anatomical representations crucial for developing robust computer vision models. By providing both clinical and phantom data, UAAL supports the creation of more accurate and reliable automation technologies for these complex procedures. This publicly accessible dataset aims to address the growing global burden of respiratory diseases by facilitating research and innovation in medical robotics and potentially reducing healthcare disparities in respiratory care.