基于嵌套U型的3D脑MRI配准网络
3D Brain MRI Registration Network Based on Nested U-Shapes
孙克雷 1童波 2潘宇1
作者信息
- 1. 安徽理工大学计算机科学与工程学院,安徽淮南 232000
- 2. 安徽理工大学人工智能学院,安徽淮南 232000
- 折叠
摘要
针对传统的基于U-Net的图像配准网络对图像细节信息提取不够精确的问题,引入了一种嵌套的U型配准网络,在网络的每级编码解码阶段引入RSU-L的U型残差模块,并且在U型网络中的解码阶段每上采样一次均生成配准场,最后将配准场进行叠加得到最终的配准形变场.在公开数据集中,与传统的ANTs、Voxel-morph 和最新的Transmorph网络相比,提出的嵌套U型网络在Dice系数上提升了1％～11％,增加了网络模型在图像配准任务上的精确度,对于临床诊断具有一定的帮助.
Abstract
To address the issue of inaccurate extraction of image details in traditional U-Net based image registration networks,a nested U-shaped registration network is introduced.The U-shaped residual module of RSU-L is introduced in each encoding and decoding stage of the network,and a registration field is generated every time the decoding stage in the U-shaped network is upsampled.Finally,the registration field is superimposed to obtain the final regis-tration deformation field.In open datasets,compared with traditional ANTs,Voxelmorph,and the latest Transmorph networks,the proposed nested U-shaped network improves the Dice coefficient by 1％to 11％,increased the accuracy of network models for image registra-tion tasks and providing some assistance for clinical diagnosis and treatment.
关键词
图像配准/嵌套结构/U型网络/配准场融合/残差结构Key words
image registration/nested structure/U-shaped network/registration field fusion/residual structure引用本文复制引用
出版年
2025
NETL
NSTL
万方数据