基于组织修复的脑肿瘤图像配准方法  

A tissue recovery-based brain tumor image registration method

作　　者：刘忠强 钟涛 曹晓欢 张煜[1,2] LIU Zhongqiang;ZHONG Tao;CAO Xiaohuan;ZHANG Yu(School of Biomedical Engineering,Southern Medical University,Guangzhou 510515,China;Guangdong Provincial Key Laboratory of Medical Image Processing,Southern Medical University,Guangzhou 510515,China;Shanghai United Imaging Intelligence Co.,Ltd.,Shanghai 200030,China)

机构地区：[1]南方医科大学生物医学工程学院,广东广州510515 [2]广东省医学图像处理重点实验室,广东广州510515 [3]上海联影智能医疗科技有限公司,上海200030

出　　处：《南方医科大学学报》2021年第2期292-298,共7页Journal of Southern Medical University

基　　金：国家自然科学基金(61671230)。

摘　　要：本文研究一种基于组织修复方式实现脑肿瘤磁共振图像与正常脑磁共振图像准确配准的方法。首先在BraTS2018数据集使用分割网络分割脑部肿瘤,然后根据肿瘤分割掩膜,使用组织修复网络模拟生成肿瘤区域内缺失的正常组织以替换肿瘤区域。最后,将修复后的脑图像与标准的正常脑图像配准。我们通过对修复前后图像和正常图像配准结果来评估我们的方法有效性。配准实验结果表明,我们提出的方法降低了脑肿瘤图像中病理变异对配准结果的影响,与传统的直接配准的算法相比,本文所提出的方法能获得更准确的配准结果,可以有效模拟生成正常组织替换肿瘤区域,从而提高脑肿瘤图像与正常大脑图像的配准准确度。We propose an algorithm for registration between brain tumor images and normal brain images based on tissue recovery.U-Net is first used in BraTS2018 dataset to segment the brain tumors,and PConv-Net is then used to simulate the generation of missing normal tissues in the tumor region to replace the tumor region.Finally,the normal brain image is registered to the tissue recovery brain image.We evaluated the effectiveness of this method by comparing the registration results of the repaired image and the tumor image corresponding to the surrounding tissues of the tumor area.The experimental results showed that the proposed method could reduce the effect of pathological variation,achieve a high registration accuracy,and effectively simulate and generate normal tissues to replace the tumor regions,thus improving the registration effect between brain tumor images and normal brain images.

关 键 词：脑肿瘤 卷积神经网络 图像分割 图像修复 配准 部分卷积 

分 类 号：R739.41[医药卫生—肿瘤] TP391.41[医药卫生—临床医学] TP183[自动化与计算机技术—计算机应用技术]