Affiliation:
1. School of Automation, Central South University, Changsha 410083, China
2. School of Xiangya Hospital, Central South University, Changsha 410075, China
Abstract
Image registration technology is a key technology used in the process of nanomaterial imaging-aided diagnosis and targeted therapy effect monitoring for abdominal diseases. Recently, the deep-learning based methods have been increasingly used for large-scale medical image registration,
because their iteration is much less than those of traditional ones. In this paper, a coarse-to-fine unsupervised learning-based three-dimensional (3D) abdominal CT image registration method is presented. Firstly, an affine transformation was used as an initial step to deal with large deformation
between two images. Secondly, an unsupervised total loss function containing similarity, smoothness, and topology preservation measures was proposed to achieve better registration performances during convolutional neural network (CNN) training and testing. The experimental results demonstrated
that the proposed method severally obtains the average MSE, PSNR, and SSIM values of 0.0055, 22.7950, and 0.8241, which outperformed some existing traditional and unsupervised learning-based methods. Moreover, our method can register 3D abdominal CT images with shortest time and is expected
to become a real-time method for clinical application.
Publisher
American Scientific Publishers
Subject
Pharmaceutical Science,General Materials Science,Biomedical Engineering,Medicine (miscellaneous),Bioengineering
Cited by
3 articles.
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