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Diffeomorphic Lung Registration Using Deep CNNs and Reinforced Learning

  • Jorge Onieva OnievaEmail author
  • Berta Marti-Fuster
  • María Pedrero de la Puente
  • Raúl San José Estépar
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11040)

Abstract

Image registration is a well-known problem in the field of medical imaging. In this paper, we focus on the registration of chest inspiratory and expiratory computed tomography (CT) scans from the same patient. Our method recovers the diffeomorphic elastic displacement vector field (DVF) by jointly regressing the direct and the inverse transformation. Our architecture is based on the RegNet network but we implement a reinforced learning strategy that can accommodate a large training dataset. Our results show that our method performs with a lower estimation error for the same number of epochs than the RegNet approach.

Keywords

Deep learning Reinforced learning Lung registration Chest computed tomography Diffeomorphism 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Applied Chest Imaging Laboratory, Department of RadiologyBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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