DeepPyramid+: medical image segmentation using Pyramid View Fusion and Deformable Pyramid Reception.

Journal: International journal of computer assisted radiology and surgery
Published Date: Jan 8, 2024

Abstract

PURPOSE: Semantic segmentation plays a pivotal role in many applications related to medical image and video analysis. However, designing a neural network architecture for medical image and surgical video segmentation is challenging due to the diverse features of relevant classes, including heterogeneity, deformability, transparency, blunt boundaries, and various distortions. We propose a network architecture, DeepPyramid+, which addresses diverse challenges encountered in medical image and surgical video segmentation.

Authors

  • Negin Ghamsarian
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland. negin.ghamsarian@unibe.ch.
  • Sebastian Wolf
    Department for Ophthalmology, Inselspital, University Hospital, University of Bern, Bern, Switzerland.
  • Martin Zinkernagel
    Department of Ophthalmology, Inselspital, Bern, Switzerland.
  • Klaus Schoeffmann
    Institute of Information Technology, Klagenfurt University, Austria.
  • Raphael Sznitman
    ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.

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