Automatic upper airway segmentation in static and dynamic MRI via deep convolutional neural networks

Lipeng Xie, Jayaram K. Udupa, Yubing Tong, Drew A. Torigian, Zihan Huang, Rachel M. Kogan, Jennifer Ben Nathan, David Wootton, Kokren Choy, Sanghun Sin, Mark E. Wagshul, Raanan Arens

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Upper airway segmentation in static and dynamic MRI is a prerequisite step for quantitative analysis in patients with disorders such as obstructive sleep apnea. Recently, some semi-Automatic methods have been proposed with high segmentation accuracy. However, the low efficiency of such methods makes it difficult to implement for the processing of large numbers of MRI datasets. Therefore, a fully automatic upper airway segmentation approach is needed. In this paper, we present a novel automatic upper airway segmentation approach based on convolutional neural networks. Firstly, we utilize the U-Net network as the basic model for learning the multi-scale feature from adjacent image slices and predicting the pixel-wise label in MRI. In particular, we train three networks with the same structure for segmenting the pharynx/larynx and nasal cavity separately in axial static 3D MRI and axial dynamic 2D MRI. The visualization and quantitative results demonstrate that our approach can be applied to various MRI acquisition protocols with high accuracy and stability.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationBiomedical Applications in Molecular, Structural, and Functional Imaging
EditorsBarjor S. Gimi, Andrzej Krol
PublisherSPIE
ISBN (Electronic)9781510640290
DOIs
StatePublished - 2021
EventMedical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11600
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Biomedical Applications in Molecular, Structural, and Functional Imaging
Country/TerritoryUnited States
CityVirtual, Online
Period2/15/212/19/21

Keywords

  • Upper airway
  • convolutional neural network
  • dynamic MRI
  • segmentation
  • sleep apnea
  • static MRI

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

Fingerprint

Dive into the research topics of 'Automatic upper airway segmentation in static and dynamic MRI via deep convolutional neural networks'. Together they form a unique fingerprint.

Cite this