TransDIR: Deformable imaging registration network based on transformer to improve the feature extraction ability.

Journal: Medical physics
Published Date: Jan 18, 2022

Abstract

PURPOSE: Imaging registration has a significant contribution to guide and support physicians in the process of decision-making for diagnosis, prognosis, and treatment. However, existing registration methods based on the convolutional neural network cannot extract global features effectively, which significantly influences registration performance. Moreover, the smoothness of the displacement vector field (DVF) fails to be ensured due to the miss folding penalty.

Authors

  • Tiejun Yang
    Key Laboratory of Grain Information Processing and Control (HAUT), Ministry of Education, Zhengzhou, China.
  • Xinhao Bai
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China.
  • Xiaojuan Cui
    Information Materials and Intelligent Sensing Laboratory of Anhui Province, Anhui University, 230601 Hefei, China; Key Laboratory of Opto-Electronic Information Acquisition and Manipulation of Ministry of Education, Anhui University, 230601 Hefei, China.
  • Yuehong Gong
    School of Information Science and Engineering, Henan University of Technology, Zhengzhou, Henan, China.
  • Lei Li
    Department of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.

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