Unsupervised domain adaptation method for segmenting cross-sectional CCA images.

Journal: Computer methods and programs in biomedicine
Published Date: Jul 22, 2022

Abstract

BACKGROUND AND OBJECTIVES: Automatic vessel segmentation in ultrasound is challenging due to the quality of the ultrasound images, which is affected by attenuation, high level of speckle noise and acoustic shadowing. Recently, deep convolutional neural networks are increasing in popularity due to their great performance on image segmentation problems, including vessel segmentation. Traditionally, large labeled datasets are required to train a network that achieves high performance, and is able to generalize well to different orientations, transducers and ultrasound scanners. However, these large datasets are rare, given that it is challenging and time-consuming to acquire and manually annotate in-vivo data.

Authors

  • Luuk van Knippenberg
    Department of Electrical Engineering, Eindhoven University of Technology, the Netherlands. Electronic address: l.a.e.m.v.knippenberg@tue.nl.
  • Ruud J G van Sloun
    Laboratory of Biomedical Diagnostics, Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands. Electronic address: r.j.g.v.sloun@tue.nl.
  • Massimo Mischi
    Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.
  • Joerik de Ruijter
  • Richard Lopata
    Department of Biomedical Engineering, Eindhoven University of Technology, the Netherlands; Department of Anesthesiology, Catharina Hospital Eindhoven, the Netherlands.
  • R Arthur Bouwman
    Department of Anesthesiology, Catharina Hospital Eindhoven, the Netherlands.

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