Nonlinear Registration of Brain Magnetic Resonance Images with Cross Constraints of Intensity and Structure  

作　　者：Han Zhou HongtaoXu Xinyue Chang Wei Zhang Heng Dong 

机构地区：[1]Digital Fujian Research Institute of Big Data forAgriculture and Forestry,Fujian Agriculture and Forestry University,Fuzhou,350002,China [2]College of Computer and Information Science,Fujian Agriculture and Forestry University,Fuzhou,350002,China

出　　处：《Computers, Materials & Continua》2024年第5期2295-2313,共19页计算机、材料和连续体（英文）

基　　金：National Natural Science Foundation of China(Grant Nos.62171130,62172197,61972093);the Natural Science Foundation of Fujian Province(Grant Nos.2020J01573,2022J01131257,2022J01607);Fujian University Industry University Research Joint Innovation Project(No.2022H6006);in part by the Fund of Cloud Computing and BigData for SmartAgriculture(GrantNo.117-612014063);NationalNatural Science Foundation of China(Grant No.62301160);Nature Science Foundation of Fujian Province(Grant No.2022J01607).

摘　　要：Many deep learning-based registration methods rely on a single-stream encoder-decoder network for computing deformation fields between 3D volumes.However,these methods often lack constraint information and overlook semantic consistency,limiting their performance.To address these issues,we present a novel approach for medical image registration called theDual-VoxelMorph,featuring a dual-channel cross-constraint network.This innovative network utilizes both intensity and segmentation images,which share identical semantic information and feature representations.Two encoder-decoder structures calculate deformation fields for intensity and segmentation images,as generated by the dual-channel cross-constraint network.This design facilitates bidirectional communication between grayscale and segmentation information,enabling the model to better learn the corresponding grayscale and segmentation details of the same anatomical structures.To ensure semantic and directional consistency,we introduce constraints and apply the cosine similarity function to enhance semantic consistency.Evaluation on four public datasets demonstrates superior performance compared to the baselinemethod,achieving Dice scores of 79.9%,64.5%,69.9%,and 63.5%for OASIS-1,OASIS-3,LPBA40,and ADNI,respectively.

关 键 词：Medical image registration cross constraint semantic consistency directional consistency DUAL-CHANNEL 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]