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4D-CT deformable image registration using multiscale unsupervised deep learning by Lei, Yang; Fu, Yabo; Wang, Tonghe; Liu, Yingzi; Patel, Pretesh; Curran, Walter J; Liu, Tian; Yang, Xiaofeng is a Medicine article available to read on EtoBox.

What is 4D-CT deformable image registration using multiscale unsupervised deep learning about?

Deformable image registration (DIR) of 4D-CT images is important in multiple radiation therapy applications including motion tracking of soft tissue or fiducial markers, target definition, image fusion, dose accumulation and treatment response evaluations. It is very challenging to accurately and quickly register 4D-CT abdominal images due to its large appearance variances and bulky sizes. In this study, we proposed an accurate and fast multi-scale DIR network (MS-DIRNet) for abdominal 4D-CT registration. MS-DIRNet consists of a global network (GlobalNet) and local network (LocalNet). GlobalNet was trained using down-sampled whole image volumes while LocalNet was trained using sampled image patches. MS-DIRNet consists of a generator and a discriminator. The generator was trained to directly predict a deformation vector field (DVF) based on the moving and target images. The generator was implemented using convolutional neural networks with multiple attention gates. The discriminator was trained to differentiate the deformed images from the target images to provide additional DVF regularization. The loss function of MS-DIRNet includes three parts which are image similarity loss, adve

Who reads 4D-CT deformable image registration using multiscale unsupervised deep learning?

Author

Lei, Yang; Fu, Yabo; Wang, Tonghe; Liu, Yingzi; Patel, Pretesh; Curran, Walter J; Liu, Tian; Yang, Xiaofeng

Publisher

Institute of Physics; IOP Publishing; Institute of Physics Publishing (ISSN 0031-9155)

Published

2020

Language

EN

Field

Medicine (Health Sciences)