融合跨阶段深度学习的脑肿瘤MRI图像分割  

Cross-stage deep-learning-based MRI fused images of human brain tumor segmentation

作　　者：夏峰 邵海见 邓星 Xia Feng;Shao Haijian;Deng Xing(School of Computer Science,Jiangsu University of Science and Technology,Zhenjiang 212003,China;Key Laboratory of Complex Engineering System Measurement and Control,School of Automation,Southeast University,Ministry of Education,Nanjing 210009,China)

机构地区：[1]江苏科技大学计算机学院,镇江212003 [2]东南大学自动化学院复杂工程系统测量与控制教育部重点实验室,南京210009

出　　处：《中国图象图形学报》2022年第3期873-884,共12页Journal of Image and Graphics

基　　金：国家自然科学基金项目(61806087,61902158)。

摘　　要：目的磁共振成像(magnetic resonance imaging,MRI)作为一种非侵入性的软组织对比成像方式,可以提供有关脑肿瘤的形状、大小和位置等有价值的信息,是用于脑肿瘤患者检查的主要方法,在脑肿瘤分割任务中发挥着重要作用。由于脑肿瘤本身复杂多变的形态、模糊的边界、低对比度以及样本梯度复杂等问题,导致高精度脑肿瘤MRI图像分割非常具有挑战性,目前主要依靠专业医师手动分割,费时且可重复性差。对此,本文提出一种基于U-Net的改进模型,即CSPU-Net(cross stage partial U-Net)脑肿瘤分割网络,以实现高精度的脑肿瘤MRI图像分割。方法CSPU-Net在U-Net结构的上下采样中分别加入两种跨阶段局部网络结构(cross stage partial module,CSP)提取图像特征,结合GDL(general Dice loss)和WCE(weighted cross entropy)两种损失函数解决训练样本类别不平衡问题。结果在BraTS(brain tumor segmentation)2018和BraTS 2019两个数据集上进行实验,在BraTS 2018数据集中的整体肿瘤分割精度、核心肿瘤分割精度和增强肿瘤分割精度分别为87.9%、80.6%和77.3%,相比于传统U-Net的改进模型(ResU-Net)分别提升了0.80%、1.60%和2.20%。在BraTS 2019数据集中的整体肿瘤分割精度、核心肿瘤分割精度和增强肿瘤分割精度分别为87.8%、77.9%和70.7%,相比于ResU-Net模型提升了0.70%、1.30%和1.40%。结论本文提出的跨阶段局部网络结构,通过增加梯度路径、减少信息损失,可以有效提高脑肿瘤分割精度,实验结果证明了该模块对脑肿瘤分割任务的有效性。Objective Human brain tumors are a group of mutant cells in the brain or skull.These benign or malignant brain tumors can be classified based on their growth characteristics and influence on the human body.Gliomas are one of the most frequent forms of malignant brain tumors,accounting for approximately 40%to 50%of all brain tumors.Glioma is classified as high-grade glioma(HGG)or low-grade glioma(LGG)depending on the degree of invasion.Low-grade glioma(LGG)is a well-differentiated glioma with a prompt prognosis.High-grade glioma(HGG)is a poorly differentiated glioma with a in qualified prognosis.Gliomas with varying degrees of differentiation are appeared following the varied degrees of peritumoral edema,edema types,and necrosis.the boundary of gliomas and normal tissues is often blurred.It is difficult to identify the scope of lesions and surgical area,which has a significant impact on surgical quality and patient prognosis.As a non-invasive and clear soft tissue contrast imaging tool,magnetic resonance imaging(MRI)can provide vital information on the shape,size,and location of brain tumors.High-precision brain tumor MRI image segmentation is challenged due to the complicated and variable morphology,fuzzy borders,low contrast,and complicated sample gradients of brain tumors.Manual segmentation is time-consuming and inconsistent.The International Association for Medical Image Computing and Computer-Aided Intervention(MICCAI)’s Brain Tumor Segmentation(BraTS)is a global medical image segmentation challenge concentrating on the evaluation of automatic segmentation methods for human brain tumors.There are four types of automatic brain tumor segmentation algorithms as mentioned below:supervised learning,semi-supervised learning,unsupervised learning,and hybrid learning.Supervised-learning-based algorithm is currently the effective method.Various depth neural network models for computer vision problems,such as Visual Geometry Group Network(VGGNet),GoogLeNet,ResNet,and DenseNet,have been presented in recent years.The

关 键 词：脑肿瘤分割 深度学习 U-Net 跨阶段局部网络结构 残差模块 

分 类 号：TP302[自动化与计算机技术—计算机系统结构]