摘要
脑出血指非外伤性脑实质内出血,发病急,进展迅速,致死率和致残率高。对于疑诊急性脑出血患者,CT为首选影像学检查手段。影像组学高通量从CT图像中提取特征信息,结合机器学习算法,能快速、准确地诊断疾病、评估病情和预测预后。本文就基于CT影像组学和机器学习脑出血研究进展进行综述。
Intracerebral hemorrhage refers to non-traumatic cerebral hemorrhage with acute onset,rapid progression,high fatality rate and disability rate.CT has become the first choice for emergency patients with suspected acute cerebral hemorrhage.Radiomics can extract feature information from CT images with high throughput,hence quickly and accurately diagnosing diseases,evaluating severity and predicting prognosis combined with machine learning algorithm.The research progresses of radiomics and machine learning base on CT for cerebral hemorrhage were reviewed in this article.
作者
张康微
魏来
孟瑾茜
王培军
ZHANG Kangwei;WEI Lai;MENG Jinqian;WANG Peijun(Department of Radiology,Tongji Hospital of Tongji University,Shanghai 200065,China)
出处
《中国医学影像技术》
CSCD
北大核心
2022年第4期604-606,共3页
Chinese Journal of Medical Imaging Technology
基金
上海市科学技术委员会科研计划(19411951400)。
