基于自注意力机制的脑血肿分割和出血量测量算法  

Cerebral Hematoma Segmentation and Bleeding Volume Measurement Based on Self⁃attention Mechanism

作　　者：李垚 余南南 胡春艾[2] 柯明池 于金扣 LI Yao;YU Nannan;HU Chunai;KE Mingchi;YU Jinkou(School of Electrical Engineering and Automation,Jiangsu Normal University,Xuzhou 221116,China;Xuzhou Central Hospital,Xuzhou 221009,China;Suqian Integrated Traditional Chinese and Western Medicine Hospital,Suqian 223899,China)

机构地区：[1]江苏师范大学电气工程及自动化学院,徐州221116 [2]徐州市中心医院,徐州221009 [3]宿迁市中西医结合医院,宿迁223899

出　　处：《数据采集与处理》2022年第4期839-847,共9页Journal of Data Acquisition and Processing

基　　金：徐州市科技计划项目(KC20164);江苏省高校自然科学研究重大项目(19KJA460010)。

摘　　要：出血量是脑出血疾病分级、治疗方案确定和预后判断的重要指标。但由于大脑结构的复杂性、血肿形态和位置的多样性,在CT影像中准确可靠地分割血肿和测量出血量极为困难。本文提出一种基于自注意力机制深度学习网络的脑血肿分割和出血量测量算法。首先,为克服大脑结构的复杂性,弥补卷积模块只能进行线性运算和提取局部特征的缺点,在分割网络编码器末端引入自注意力模块,通过高阶运算,提取图像全域的特征关联特性,从全局角度提取血肿;然后引入通道和空间注意力模块,通过训练学习得到各个通道和特征区域上的权重,通过该权重突出有用信息,抑制无用信息;最后,根据脑出血患者多层CT影像切片的血肿分割结果,计算出血量。在真实脑出血CT影像数据集上的实验结果表明,本文算法在多种情况下的脑血肿分割和出血量测量上均取得了较好的效果,即使在形状不规则或贴近颅骨的情况下,本文算法仍然较为有效。Hemorrhage volume is an important indicator for the grading of intracerebral hemorrhage disease,the determination of treatment options,and the judgment of prognosis.However,because of the complexity of the brain structure and the variety of morphology and location of the hematoma,accurate and reliable segmentation of the hematoma and measurement of the amount of hemorrhage are extremely difficult.This paper presents an algorithm for cerebral hematoma segmentation and blood volume measurement based on a self-attention mechanism deep learning network.First,to overcome the complexity of brain structure and make up for the shortcomings that convolution module can only perform linear operations and extract local features,a self-attention module is introduced at the end of the encoder of the segmentation network,and through higher order operations,the feature association properties of the whole domain of the image are extracted and the hematoma is extracted from a global perspective.Second,a channel and spatial attention module is introduced to obtain weights on the individual channels and feature regions through training learning,by which useful information is highlighted and useless information is suppressed.Finally,the hemorrhage volume is calculated based on the hematoma segmentation results of multislice CT imaging slices in patients with intracerebral hemorrhage.The experimental results on the real CT imaging data set of intracerebral hemorrhage show that the proposed algorithm achieves better results on cerebral hematoma segmentation and hemorrhage volume measurement in multiple cases,and even is still relatively effective in the case of irregular shape or close to skull.

关 键 词：脑出血 图像分割 出血量 深度学习 自注意力机制 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]