人工智能在非增强CT图像中颅内出血早期检出和血肿分割的研究进展  

Advances in artificial intelligence be applied to early diagnose intracranial hemorrhage and hematoma segmentation

作　　者：胡平 鄢腾峰 周海柱 祝新根[1] Ping Hu;Tengfeng Yan;Haizhu Zhou;Xingen Zhu(Department of Neurosurgery,the Second Affiliated Hospital of Nanchang University,Nanchang 330006,China;School of Physics and Technology,Wuhan University,Wuhan 430000,China)

机构地区：[1]南昌大学第二附属医院神经外科,南昌330006 [2]武汉大学物理科学与技术学院,武汉430000

出　　处：《中华脑血管病杂志（电子版）》2023年第4期410-416,共7页Chinese Journal of Cerebrovascular Diseases(Electronic Edition)

基　　金：基金资助:国家自然科学基金项目(82172989);中央引导地方科技发展资金项目(S2021KJCXG0005);江西省重点研发计划项目(20212BBG71012);江西省研究生创新专项资金项目(YC2023-B081)。

摘　　要：颅内出血(ICH)的早期检出对挽救患者神经功能乃至生命至关重要,而精确量化血肿体积则将为临床决策提供重要依据。非增强CT作为ICH的标准成像方式,随着人工智能(AI)的不断发展,目前越来越多的研究将AI应用于ICH非增强CT图像的早期检出和血肿分割。本文就近年来应用AI在ICH检出、识别出血类型、血肿分割方面的研究进展进行综述,以期验证AI是否可以构建准确的ICH自动分诊系统以降低误差率,为辅助临床医师制定准确的诊疗方案提供依据。Early detection of intracranial hemorrhage is crucial to saving patients'neurological function and even life,and accurate quantification of hematoma volume will provide an essential basis for clinical decision-making.Non-contrast computed tomography is a standard imaging method for intracranial hemorrhage.With the continuous development of artificial intelligence,an increasing number of studies are applying artificial intelligence to the early detection and segmentation of non-contrast CT images of intracranial hemorrhage.This article reviews the recent research progress of artificial intelligence in the detection,subtype classification,and hematoma segmentation of intracranial hemorrhage,in order to verify whether artificial intelligence can construct an accurate automatic classification system for intracranial hemorrhage to reduce the error rate,and to provide a basis for assisting clinicians in making accurate diagnosis and treatment plans.

关 键 词：颅内出血 人工智能 深度学习 诊断 

分 类 号：R743.35[医药卫生—神经病学与精神病学]