摘要
针对磁共振成像脑肿瘤分割存在的肿瘤空间信息变化大与精细标注样本数量少的问题,提出一种基于多尺度伪影生成对抗网络的脑肿瘤影像分割方法。该方法采用三维U-Net模型来获取脑肿瘤分割结果并充当生成器,引入三维PatchGAN作为判别器来评判U-Net输出的脑肿瘤结果与真值标签,通过对抗学习方式来进行模型训练。为提升脑肿瘤分割效果,在生成器编码阶段引入伪影模块,使得在卷积过程中能够捕获到更丰富的深度特征而提升生成器的脑肿瘤生成结果;同时,在解码过程中采用多尺度特征融合方式来有效整合脑肿瘤的浅层信息与深层信息,并在对抗学习中进一步提升分割性能。在公开的BraTS2019-2020数据集上对该方法进行了评估,实验结果验证了所提出方法在脑肿瘤分割任务中的有效性,在两个验证集上获得的全肿瘤、核心肿瘤和增强肿瘤分割Dice值分别为0.902/0.903、0.836/0.826和0.77/0.782。
Brain tumors are abnormal cells that grow in the brain or skull,and malignant brain tumors always cause great danger to the life and health of patients.Magnetic Resonance Imaging(MRI) can produce high-quality brain images without damage and skull artifacts,and it is currently one of the main technologies for the diagnosis and treatment of brain tumors.Meanwhile,the automatic segmentation of MRI brain tumor lesion regions is of great significance for the clinical diagnosis,surgical planning,and postoperative evaluation of brain tumor patients.However,due to the complexity and diversity of brain tumor images and the difficulty in obtaining the large-scale high-quality brain tumor segmentation dataset,it is still a difficult task to achieve high-precision automatic segmentation of MRI brain tumors.In recent years,with the breakthrough development of deep learning in computer vision tasks,it has also been successfully applied in the field of medical image analysis,and has achieved significant performance improvement in a number of medical image analysis tasks.Among them,U-Net,with its simple architecture and high performance,has become a mainstream model to solve a series of medical image segmentation tasks,including brain tumor segmentation.To this end,according to the advantages of the U-Net network architecture,focusing on issues of large variation of tumor spatial information and small number of finely labeled samples,a novel brain tumor image segmentation method,called Multi-scale Ghost Generation Adversarial Network(MG2AN),is proposed by combining U-Net with unsupervised generative adversarial network.MG2AN leverages the 3D U-Net model as a generator to obtain brain tumor segmentation results,and introduces 3D PatchGAN corresponding to multi-scale feature information of the generator as a discriminator to judge brain tumor segmentation results and ground truth,and the whole model is trained by adversarial learning.To improve the MRI brain tumor segmentation effect,a ghost module is introduced in the encoding st
作者
张睦卿
韩雨童
陈柏年
张建新
ZHANG Muqing;HAN Yutong;CHEN Bonian;ZHANG Jianxin(School of Computer Science and Engineering,Dalian Minzu University,Dalian 116600,China;Institute of Machine Intelligence and Biocomputing,Dalian Minzu University,Dalian 116600,China)
出处
《光子学报》
EI
CAS
CSCD
北大核心
2023年第8期194-205,共12页
Acta Photonica Sinica
基金
国家自然科学基金(No.61972062)
辽宁省应用基础研究计划(No.2023JH2/101300191)。
