Joint liver and hepatic lesion segmentation in MRI using a hybrid CNN with transformer layers.

Journal: Computer methods and programs in biomedicine
Published Date: Jun 7, 2023

Abstract

UNLABELLED: Backgound and Objective: Deep learning-based segmentation of the liver and hepatic lesions therein steadily gains relevance in clinical practice due to the increasing incidence of liver cancer each year. Whereas various network variants with overall promising results in the field of medical image segmentation have been successfully developed over the last years, almost all of them struggle with the challenge of accurately segmenting hepatic lesions in magnetic resonance imaging (MRI). This led to the idea of combining elements of convolutional and transformer-based architectures to overcome the existing limitations.

Authors

  • Georg Hille
    Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany. Electronic address: georg.hille@ovgu.de.
  • Shubham Agrawal
    Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany.
  • Pavan Tummala
    Department of Simulation and Graphics, Otto-von-Guericke University, Magdeburg, Germany.
  • Christian Wybranski
    Department of Radiology, University Hospital of Magdeburg, Magdeburg, Germany.
  • Maciej Pech
    Department of Radiology and Nuclear Medicine, University Hospital Magdeburg, Germany; 2nd Department of Radiology, Medical University of Gdansk, Poland. Electronic address: maciej.pech@med.ovgu.de.
  • Alexey Surov
    Department of Diagnostic and Interventional Radiology, University of Leip-zig, Leipzig, Germany.
  • Sylvia Saalfeld
    Department of Simulation and Graphics, Otto von Guericke University Magdeburg, Germany; Research Campus STIMULATE, Otto von Guericke University Magdeburg, Germany.

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