Landmark Detection using Transformer Toward Robot-assisted Nasal Airway
  Intubation
Tianhang Liu Hechen Li Long Bai Yanan Wu An Wang Mobarakol Islam Hongliang Ren
Robot-assisted airway intubation application needs high accuracy in locating
targets and organs. Two vital landmarks, nostrils and glottis, can be detected
during the intubation to accommodate the stages of nasal intubation. Automated
landmark detection can provide accurate localization and quantitative
evaluation. The Detection Transformer (DeTR) leads object detectors to a new
paradigm with long-range dependence. However, current DeTR requires long
iterations to converge, and does not perform well in detecting small objects.
This paper proposes a transformer-based landmark detection solution with
deformable DeTR and the semantic-aligned-matching module for detecting
landmarks in robot-assisted intubation. The semantics aligner can effectively
align the semantics of object queries and image features in the same embedding
space using the most discriminative features. To evaluate the performance of
our solution, we utilize a publicly accessible glottis dataset and
automatically annotate a nostril detection dataset. The experimental results
demonstrate our competitive performance in detection accuracy. Our code is
publicly accessible.