Scalable quorum-based deep neural networks with adversarial learning for automated lung lobe segmentation in fast helical free-breathing CTs.

Journal: International journal of computer assisted radiology and surgery
Published Date: Aug 10, 2021

Abstract

PURPOSE: Fast helical free-breathing CT (FHFBCT) scans are widely used for 5DCT and 5D Cone Beam imaging protocols. For quantitative analysis of lung physiology and function, it is important to segment the lung lobes in these scans. Since the 5DCT protocols use up to 25 FHFBCT scans, it is important that this segmentation task be automated. In this paper, we present a deep neural network (DNN) framework for segmenting the lung lobes in near real time.

Authors

  • Bradley Stiehl
    University of California, Los Angeles, CA, 90024, USA. BStiehl@mednet.ucla.edu.
  • Michael Lauria
    University of California, Los Angeles, CA, 90024, USA.
  • Kamal Singhrao
    David Geffen School of Medicine, University of California, Los Angeles, 10833 Le Conte Ave, Los Angeles, 90095, CA, USA.
  • Jonathan Goldin
    University of California Los Angeles David Geffen School of Medicine, Los Angeles, California, USA.
  • Igor Barjaktarevic
    University of California, Los Angeles, CA, 90024, USA.
  • Daniel Low
    University of California, Los Angeles, CA, 90024, USA.
  • Anand Santhanam
    Department of Radiation Oncology, UCLA, 200 Medical Plaza, Suite B265, Los Angeles, CA, 90095, USA.

Keywords

External Resources

Popular Topics
Recent Journals