A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery

Ameen Amanian1,2, Aseem Jain1,3, Yuliang Xiao1, Chanha Kim1, Andy S. Ding1, Manish Sahu1, Russell Taylor1, Mathias Unberath1, Bryan K. Ward1, Deepa Galaiya1, Masaru Ishii1, Francis X. Creighton1
1Johns Hopkins University, 2University of British Colombia, 3University of Cincinnati
Wiley Paper DOI Code

Given the CT scans, our network is able to segment ET and ICA, and compute the distance heatmap between them

Abstract

Objective: Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures.
Methods: From a database of 30 CT scans, 60 ET and ICA pairs were manually segmented and used to train an nnU-Net model, a DL segmentation framework. These segmentations were also used to develop a quantitative tool to capture the magnitude and location of the minimum distance point (MDP) between ET and ICA. Performance metrics for the nnU-Net automated segmentations were calculated via the average Hausdorff distance (AHD) and dice similarity coefficient (DSC).
Results: The AHD for the ET and ICA were 0.922 and 0.246 mm, respectively. Similarly, the DSC values for the ET and ICA were 0.578 and 0.884. The mean MDP from ET to ICA in the cartilaginous region was 2.6 mm (0.7-5.3 mm) and was located on average 1.9 mm caudal from the bony cartilaginous junction.
Conclusion: This study describes the first end-to-end DL pipeline for automated ET and ICA segmentation and analyzes distances between these structures. In addition to helping to ensure the safe selection of patients for ET dilation, this method can facilitate large-scale studies exploring the relationship between ET pathologies and the 3D shape of the ET.

Heatmap Visual Results

Poster

BibTeX

@article{amanian2024deep,
  title={A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery},
  author={Amanian, Ameen and Jain, Aseem and Xiao, Yuliang and Kim, Chanha and Ding, Andy S and Sahu, Manish and Taylor, Russell and Unberath, Mathias and Ward, Bryan K and Galaiya, Deepa and others},
  journal={Otolaryngology--Head and Neck Surgery},
  publisher={Wiley Online Library}
}