Author:
Deng Liwei,Zou Yanchao,Huang Sijuan,Yang Xin,Wang Jing
Abstract
Abstract
Deformable registration of medical images based on deep
learning has been the research focus this year. Convolutional Neural
Network (CNN) and the transformer are the most common backbone and
have been shown to enhance registration accuracy. However, CNN lacks
the ability to contact long-distance information, and the
transformer lacks the ability to capture local
information. Whichever subtle feature loss may lead to disastrous
consequences in the analysis of clinical medicine. This paper
presented a novel registration network named Information
Complementation Network (ICN). We aim to improve the registration
accuracy by complementing the lost information. Pure transformers
can establish long-distance spatial information about the
image. Proposed meshing patch embedding can minimize the loss of
local information and expand the receptive field to extract
long-distance information. The dual-path decoder in ICN is designed
to restore information furthest. We experimented on 3D brain MRI
data and quantitatively compared several excellent registration
models. Compared with conventional methods, the dice coefficient
increased by 3%. Compared with the advanced methods, the dice
coefficient increased by 1%. The number of foldings was reduced by
about 50% without any loss of registration accuracy. Each
evaluation metric of the trained models on liver CT images was
higher than other methods. By fully complementing the lost or
invalid information, ICN achieved higher registration accuracy and
smoother deformation field. The innovation and contribution of this
paper have the potential to be applied to clinical research or
medical image processing.
Subject
Mathematical Physics,Instrumentation