A Joint Super-Resolution and Deformable Registration Network for 3D Brain Images

被引:0
|
作者
Lan, Sheng [1 ]
Guo, Zhenhua [2 ]
机构
[1] Tsinghua Univ, Dept Automat, Beijing, Peoples R China
[2] Tsinghua Shenzhen Int Grad Sch, Shenzhen, Peoples R China
来源
2020 25TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR) | 2021年
关键词
D O I
10.1109/ICPR48806.2021.9412553
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper, we propose a joint network for 3D brain images registration with super-resolution to reduce deformable image registration errors caused by low resolution. Basically, the task of deformable image registration is to find the displacement field between the reference image and the moving image. Many research works have been done for deformable image registration, under the assumption that the image resolution is high enough. However, due to the limited level of current acquisition instruments, the resolution of images is not high enough usually. As low resolution images might cause large registration errors, this paper presents a new approach that performs joint super-resolution and deformable image registration. Experiments with 3D brain images show that the joint network does help to reduce the registration errors significantly.
引用
收藏
页码:173 / 179
页数:7
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