CN109997200B - 脑中风诊断以及预后预测方法及系统 - Google Patents
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Abstract
本发明提供脑中风诊断以及预后预测方法及系统,能够更加准确的诊断脑中风以及可靠性地预测脑中风患者状态。脑中风诊断以及预后预测系统包括:影像获取部,接收包括人脑的至少一部分的多个影像;影像排列部,以标准脑影像为基准排列多个影像;病变区域检出以及映射部,从多个影像分别检出病变区域,将多个影像映射于检出到的病变区域生成一个映射影像;整合以及校正部,缩放映射影像,以整合于标准脑影像,并对映射影像执行影像校正;三维影像生成部,将映射影像容纳于三维数据空间,以生成三维病变影像;脑中风诊断部,基于三维病变影像诊断脑中风。
Description
技术领域
本发明涉及脑中风诊断以及预后预测方法及其系统,更详细地说,涉及能够更加准确地诊断脑中风以及可靠性地预测脑中风患者状态的脑中风诊断以及预后预测方法及系统。
背景技术
脑中风(stroke or apoplexy)是指由脑血流异常突然引起的局部神经功能缺损症状。脑中风是症状的术语,当它被称为医学疾病时,它被称为脑血管疾病。中风大致分为脑梗塞和脑出血。
现有的脑中风诊断是通过熟练地专家判定原因,评估重症程度。用作国际基准的脑中风原因分类法正在灵活利用TOAST(Trial of Org 10172 in Acute StrokeTreatment)基准。TOAST分类法是直到现在最普遍使用的分类法。根据发生机制分为大动脉粥样硬化(large artery atherosclerosis)、心源性栓塞(cardioembolism)、小动脉闭塞(small artery occlusion)、其他原因(other causes)、原因不明(undetermined cause)等。
此外,已知早期神经功能缺损程度(重症程度)作为脑中风预后的决定因素是非常重要的。尽管使用各种标准来检出初期神经功能缺损程度,但是事实是不足以同时满足检出的合理性、可靠性和检出方便性等。而重症程度的评估正在通过NIHSS区分。
已知NIHSS(National Institute of Health Stroke Scale,神经功能缺损评分)是合理性与可靠性高,因此在国内使用最多。在临床研究中,有必要定量检出神经功能缺损的严重程度,但实际临床上,达不到定量检出与分析,大部分情况是叙述性地检出并记录。另外,还有一种方法是可以在三个月后使用mRS标准评估脑中风治疗后的状态。
另一方面,作为现有技术正在提出了评估组织病症、诊断病理学病症等的技术。例如,具有韩国公开专利公报第10-2016-0058812号(2016年5月25日)的“用于诊断疾病的影像分析技法”;韩国公开专利公报第10-2015-0098119号(2015年8月27日)的“用于在医学成像中去除假阳性病变候选的系统和方法”;韩国公开专利公报第10-2015-0108701号(2015年9月30日)的“用于医学图像中的解剖学元素视觉化系统和方法”等。
在所述公开专利公报第10-2016-0058812号中公开了用于评估组织病症、诊断病理学病症或者该病症的预后以及风险评估的方法。所述方法包括:影像获取模块,接收包括动物或者人体组织的至少一部分的影像;边界获取模块,在所述获取的影像显示分析区域;特征提取模块,从所述分析区域提取定量信息;以及机械学习模块,接收所述提取的信息,并且适用至少一个检出算法,以评估所述组织的病症。
上述的现有文献公开了用于执行对一般疾病或者病变的诊断的技术,并非用于脑中风诊断以及预后预测。
另一方面,为了诊断脑中风、预后预测,对大量MRI成像资料和患者临床信息都要考虑,因此不仅用于诊断的所需时间长,而且根据医务人员的熟练程度,诊断结果可能存在较大差异。
因此有必要提供如下的技术:快速诊断脑中风的同时还提供将诊断结果偏差最小化的结果,进而提高医务人员的最终诊断与预后预测的准确度,从而采取最恰当的医疗措施。
发明内容
(要解决的问题)
本发明是考虑上述观点而提出的,目的在于提供如下的脑中风诊断以及预后预测方法:准确地诊断脑中风,可靠性地预测脑中风患者状态。
本发明的另一目的在于提供利用脑中风诊断以及预后预测方法的脑中风诊断以及预后预测系统。
(解决问题的手段)
根据本发明的一实施例,脑中风诊断以及预后预测系统包括,影像获取部,接收包括人脑的至少一部分的多个影像;影像排列部,以标准脑影像为基准排列所述多个影像;病变区域检出以及映射部,从所述多个影像分别检出病变区域,将所述多个影像映射于所述检出的病变区域生成一个映射影像;整合以及校正部,以缩放所述映射影像整合于标准脑影像,并对所述映射影像执行影像校正;三维影像生成部,以将所述映射影像容纳于三维数据空间,生成三维病变影像;脑中风诊断部,基于所述三维病变影像诊断脑中风。
所述病变区域检出以及映射部基于所述多个影像的整合位置信息,可将所述映射的病变位置整合于所述标准脑影像。
在生成三维病变影像之前,所述三维影像生成部对所述病变影像可根据有无病变将所述病变影像的像素信息二进制化,并且调节所述病变影像的大小。
所述脑中风诊断部可利用深层神经网络提取所述容纳的三维病变影像的特征,灵活利用所述提取的三维病变影像,基于训练的深层神经网络诊断脑中风。
所述深层神经网络可包括三维卷积神经网络(convolutional neural network:CNN)。
所述脑中风诊断部可分类所述诊断的脑中风的重症程度。
所述脑中风诊断部从所述病变影像预测三周以内的病危风险,并且可预测规定时间之后的患者状态。
所述多个影像可以是MRI影像;所述MRI影像可包括:扩散加权成像(Diffusionweighted imaging,DWI)、液体衰减反转恢复(Fluid Attenuated Inversion Recovery,FLAIR)影像、梯度回波(Gradient Echo,GE)影像、T2加权像(T2weighted image,T2)。
用于解决所述技术课题的本发明的以形态的方法包括如下的步骤:获取脑阔人脑的至少一部分的多个影像;以标准脑为基准排列所述多个影像;从所述多个影像分别检出病变区域,并且映射于所述检出的病变区域生成一个映射影像;以缩放所述映射影像整合于标准脑影像,并且对所述映射影像执行影像校正;以将所述映射影像容纳于三维数据空间,生成三维病变影像;以及利用深层神经网络提取所述三维病变影像的特征,灵活利用所述提取的三维病变影像,基于训练的深层神经网络诊断脑中风。
在一实施例中,所述生成一个映射影像的步骤包括基于所述整合的位置信息将所述映射的病变的位置整合于所述标准脑影像的步骤。
在一实施例中,脑中风诊断以及预后预测方法还包括如下的步骤:在生成所述三维病变影像之前对所述病变影像根据有无病变将所述病变影像的像素信息二进制化,并调节所述病变影像的大小。
在一实施例中,所述诊断脑中风的步骤可利用三维卷积神经网络(convolutionalneural network:CNN)提取所述容纳的三维病变影像的信息的特征的步骤。
在一实施例中,所述诊断脑中风的步骤可分类所述诊断的脑中风的重症程度。
在一实施例中,所述诊断脑中风的步骤可从所述病变影像预测三周以内的病危风险,并且预测规定时间之后的患者状态。
(发明的效果)
根据本发明的实施例,按等级将脑中风的原因和重症程度与准确度一同可视化,进而可提供定量和统计性的结果,这将成为医生的最终诊断的参考并且能够对患者进行论证性说明。
附图说明
图1是本发明一实施例的脑中风诊断以及预后预测系统的框图。
图2是示意性示出本发明的三维数据空间的图面。
图3示出本发明的一实施例的脑中风诊断部的框图。
图4是示出深层神经网络的示例的图面。
图5是本发明的一实施例的脑中风诊断以及预后预测方法的流程图。
图6是本发明的一实施例的脑中风诊断方法的流程图。
具体实施方式
本发明可施加各种变化,并且可具有各种实施例,而且将特定实施例示例于图面,并且将要在详细说明中进行详细的说明。与附图一同参照详细后述的实施例将明确本发明的效果以及特征、达成方法。但是本发明不限于在以下公开的实施例,而是可实现各种形态。
以下,参照附图详细说明本发明的实施例,在参照附图说明时,相同或者对应的构成要素赋予相同的附图标记,并且省略对此的重复说明。
图1是本发明一实施例的脑中风诊断以及预后预测系统的框图。
参照图1,本发明的一实施例的脑中风诊断以及预后预测系统100包括:影像获取部110、影像排列部120、病变区域检出以及映射部130、整合以及校正部140、三维影像生成部150以及脑中风诊断部160。
影像获取部110获取在诊断脑中风时使用的医疗影像。具体地说,影像获取部110从医疗成像仪器获取MR(Magnetic Resonance,磁共振)影像(MRI:Magnetic ResonanceImaging(磁共振成像))。在一实施例中,MR影像包括:扩散加权成像(Diffusion weightedimaging,DWI)、液体衰减反转恢复(Fluid Attenuated Inversion Recovery,FLAIR)影像、梯度回波(Gradient Echo,GE)影像以及T2加权像(T2weighted image,T2)。也就是说,影像获取部110使用利用扩散加权成像(Diffusion weighted imaging,DWI)、液体衰减反转恢复(Fluid Attenuated Inversion Recovery,FLAIR)影像、梯度回波(Gradient Echo,GE)影像、T2加权像(T2weighted image,T2)。也就是说,影像获取部110作为用于诊断的MRI影像使用利用扩散加权成像(DWI)、T2加权像、FLAIR影像、T1加权像的4个不同序列获取的MR影像。影像获取部110向影像排列部120传达获取的医疗影像。
影像排列部120按照MNI(Montreal Neurological Institute,蒙特利尔神经病学研究所)标准脑影像的坐标将多个影像标准化,并且以能够用坐标显示的标准脑影像为基准通过线性移动以及旋转校正脑的位置与方向。即,影像排列部120以MNI模板(template)为基准对输入的MR影像执行标准化。在此,标准脑影像是用于诊断脑中风的比较影像,使用于在分析患者的脑影像时判断有无病变以及重症程度。标准脑影像根据人种、性别、年龄有所不同。例如,标准脑影像可以是韩国人的标准脑影像。
病变区域检出以及映射部130针对多个影像检出病变区域并执行映射。具体地说,病变区域检出以及映射部130从所述多个影像分别检出病变区域,映射于所述检出的病变区域生成一个映射影像。另外,病变区域检出以及映射部130基于所述影像的整合位置信息将所述映射的病变的位置整合于所述标准脑影像。例如,病变区域检出以及映射部130检出病变区域的信息,将病变区域的信息能够以二进制(binary)形式输出于MINI空间(space)上。
整合以及校正部140缩放所述映射影像以整合于标准脑影像,并且对于所述映射影像执行影像校正。具体地说,整合以及校正部140通过线性或者非线性变形患者的脑的大小来整合于标准脑影像,以此时的整合的位置信息为根据将映射的病变的位置整合到标准脑影像的坐标,之后校正影像信息。影像校正包括去除包含于影像中的噪音或者调整亮度的动作等。
三维病变影像生成部150将所述映射影像容纳于三维数据空间,以生成三维病变影像。另外,三维病变影像生成部150可预处理整合于标准脑坐标的病变的映射影像。具体地说,三维病变影像生成部150在预处理映射影像时,根据有无病变用{1,0}将像素信息二进制化,并且可根据需要调节病变影像的大小。例如,病变影像的大小可缩小至原来大小的1/2或者1/4。例如,三维病变影像生成部150以平均影像为基准再排列所有影像,按照MNI(Montreal Neurological Institute)标准脑影像的坐标将患者的脑标准化,并且使用长、宽、高分别是8mm的正态分布内核将数据值平坦化,通过这一连串的过程预处理影像数据。
从各个序列得到的MR影像重建为三维数据空间,并且可以由三维坐标空间显示各个病变区域。另外,从各个序列得到的MR影像在预处理步骤中根据重要程度基于荷载以在三维空间中整合为一个病变图。
三维病变影像生成部150按照用于获取各个影像的序列创建三维数据空间来容纳完成必要的预处理的映射影像,以生成三维病变影像。图2是图2是示意性示出本发明的三维数据空间的图面。如图2所示,三维容纳空间被分配成整合到标准脑之后的各个影像具备的横像素数量×竖像素数量×包括的脑切片数量。在采样层中保存的值是只具有病变信息的图像的情况下,使用平均值,若是原本的MR数据,则使用最大值。
脑中风诊断部160以所述三维病变影像为基础分析脑中风原因,诊断脑中风。
图3示出本发明的一实施例的脑中风诊断部的框图。
参照图3,脑中风诊断部160包括:三维病变影像特征提取部161、脑中风原因分类部162、重症程度分类部163、三周内风险预测部164以及患者状态预测部165。
三维病变影像特征提取部161可利用深层神经网络提取容纳于三维数据容纳空间的三维病变影像的特征。图4是示出深层神经网络的示例的图面。如图4所示,在深层神经网络的设计过程中,根据分类以及分析内容以正方形或者各向异性设定数据过滤器的大小。也就是说,所述深层神经网络可以是三维卷积神经网络(3-Dimensional ConvolutionalNeural Netw ork,CNN)。
脑中风原因分类部162以所述提取的三维病变影像的特征为基础分类脑中风原因,进而可诊断脑中风。脑中风的原因分类使用国际基准的TO AST分类基准。具体地说,脑中风原因分类部162在以国际基准的TOAS T分类中主要分大动脉粥样硬化(large arteryatherosclerosis)、心源性栓塞(cardioembolism)、小动脉闭塞(small arteryocclusion),对于各个分类结果难以用某一分类基准判定的情况,分类为其他疾病。
重症程度分类部163可分类所述诊断的脑中风的重症程度。重症程度分类是以国际基准的NIHSS分类为基础被执行。重症程度分类部163为了脑中风的重症程度分类,以国际基准的NIHSS基准为基础构成分类,且为了判定的简便性,可分为以下4个阶段。重症程度分类部163将符合NIHSS的0的基准分类“无症状”;将NIHSS分数在1~4范围的分类为“低重症程度”;将NIHSS分数在5~15范围的分类为“普通重症程度”;将NIHSS分数在16~20范围的分类为“危险重症程度”;NIHSS分数在21~42范围的分类为“严重重症程度”。
三周内风险预测部164从所述病变影像预测三周内的病危风险。三周内风险预测部164以百分比预测患者住院后三周以内患者状态的恶化可能性风险,将风险率在90%以上分类为非常高风险,将70%~90%的风险率分类为高风险率;将30%~70%的风险率分类为普通风险率;不足30%的分类为低风险率。
另外,患者状态预测部165预测规定时间之后的患者状态。具体地说,状态预测部165基于国际基准mRS分类规定执行三个月之后的患者状态分类。所述脑中风原因分类部162、重症程度分类部163以及患者状态预测部165可根据机制结构决定并再建各个分类基准的得分范围并使用。
如上所述,根据本发明的脑中风诊断以及预后预测系统在准备诊断书以及预后预测报告书时可按照分类的等级分别的准确度一同将脑中风分类以及分析的内容视觉化,进而定量、统计性的提供结果,并成为最终诊断的参考,能够对患者进行论证说明。
如上所述,在本发明中灵活利用深度学习算法,利用指导学习的人工智能诊断以及预测软件,按照脑中风的原因与重症程度分类,提供能够预测住院后三周内的恶化风险与三个月之后的患者状态的平台。
图5是本发明的一实施例的脑中风诊断以及预后预测方法的流程图。首先,脑中风诊断以及预后预测系统在步骤210中获取包括人脑的至少一部分的多个影像。所述多个影像可以是MR影像,MR影像包括:扩散加权成像、液体衰减反转恢复影像、梯度回波影像以及T2加权像。
然后,脑中风诊断以及预后预测系统在步骤220中以标准脑为基准排列所述多个影像。具体地说,脑中风诊断以及预后预测系统按照MNI(Montreal NeurologicalInstitute)标准脑影像的坐标将所述多个影像标准化,以可用坐标显示的标准脑影像为基准线性通过移动以及旋转校正脑的位置与方向。
脑中风诊断以及预后预测系统在步骤230中从所述多个影像分别检出病变区域,映射于所述检出的病变区域生成一个映射影像。
然后,脑中风诊断以及预后预测系统在步骤240中缩放所述映射影像,以整合于标准脑影像,并对所述映射影像执行影像校正。具体地说,脑中风诊断以及预后预测系统将患者的脑大小线性或者非线性变形来整合于标准脑影像,将以此时整合的位置信息为根据将映射的病变位置整合于标准脑影像的坐标之后执行影像校正。
然后,脑中风诊断以及预后预测系统在步骤250中将所述映射影像容纳于三维数据空间,以生成三维病变影像。另外,虽未示出,但是脑中风诊断以及预后预测系统可预处理整合于标准脑坐标的病变的映射影像。在预处理映射影像时,脑中风诊断以及预后预测系统根据有无病变用{1,0}将像素信息二进制化,并且可根据需要调节病变影像的大小。在这一情况下,脑中风诊断以及预后预测系统按照用于获取各个影像的序列分别创建三维数据空间来容纳完成预处理的映射影像,以生成三维病变影像。
然后,脑中风诊断以及预后预测系统在步骤260中从所述三维病变影像中基于深层神经网络诊断脑中风。
图6是本发明的一实施例的脑中风诊断方法的流程图。
参照图6,脑中风诊断以及预后预测系统在步骤310中利用深层神经网络可提取容纳与三维数据容纳空间的三维病变影像的特征。所述深层神经网络可以是三维卷积神经网络(Convolutional Neural Network,CNN)。
脑中风诊断以及预后预测系统在步骤320中以所述提取的三维病变影像的特征为基础分类脑中风原因。脑中风原因分类使用国际基准的TOAS T分类基准。另外,脑中风诊断以及预后预测系统在步骤330中分类所述诊断的脑中风重症程度。重症程度分类能够以国际基准的NIHSS分类为基准执行。
脑中风诊断以及预后预测系统在步骤340中从所述病变影像预测三周内的病危风险。具体地说,脑中风诊断以及预后预测系统在步骤340中以百分比预测患者住院后三周以内患者状态的恶化可能性危险,将风险率在90%以上分为极高风险,将70%~90%的风险率分为高风险;将30%~70%的风险率分为普通风险;不足30%的分为低风险。
还有,脑中风诊断以及预后预测系统在步骤350中预测规定时间之后的患者状态。具体地说,脑中风诊断以及预后预测系统基于国际基准mR S分类规定执行三个月之后的患者状态分类。
根据本发明的实施例,灵活利用深度学习算法,利用指导学习的人工智能诊断以及预测软件,按照脑中风的原因与重症程度分类,并且提供能够与住院后三周内的恶化风险与三个月之后的患者状态的平台。
据此,根据本发明的实施例,可准确的诊断脑中风,可靠性地的预测脑中风患者的状态。
另一方面,在本发明的详细说明中通过附图以参照的优选实施例为中心进行了详细的说明,但是在不超出本发明的范围的限度内当然可实施各种变形。因此,不得将本发明的范围局限于说明的实施例,应该由权利要求范围决定,不仅如此还由与该权利要求范围同等的决定。
Claims (11)
1.一种脑中风诊断以及预后预测系统,根据脑中风诊断以及预后预测方法,其特征在于,包括:
影像获取部,接收包括人脑的至少一部分的多个影像;
影像排列部,以标准脑影像为基准排列所述多个影像;
病变区域检出以及映射部,从所述多个影像分别检出病变区域,将所述多个影像映射于所述检出的病变区域生成一个映射影像;
整合以及校正部,以缩放所述映射影像整合于标准脑影像,并对所述映射影像执行影像校正;
三维影像生成部,以将所述映射影像容纳于三维数据空间,生成三维病变影像;
脑中风诊断部,基于所述三维病变影像诊断脑中风,
所述脑中风诊断部包括:
三维病变影像特征提取部,其利用深层神经网络提取容纳于所述三维数据空间的三维病变影像的特征;
脑中风原因分类部,其以所述提取的三维病变影像的特征为基础分类脑中风原因;
重症程度分类部,其分类所述诊断的脑中风的重症程度;
三周内风险预测部,其从所述病变影像预测三周内的病危风险;及
患者状态预测部,其预测规定时间之后的患者状态。
2.根据权利要求1所述的脑中风诊断以及预后预测系统,其特征在于,
所述病变区域检出以及映射部基于所述多个影像的整合位置信息,将所述映射的病变位置整合于所述标准脑影像。
3.根据权利要求1所述的脑中风诊断以及预后预测系统,其特征在于,
在生成三维病变影像之前,所述三维影像生成部对所述病变影像根据有无病变,将所述病变影像的像素信息二进制化,并且调节所述病变影像的大小。
4.根据权利要求1所述的脑中风诊断以及预后预测系统,其特征在于,
所述脑中风诊断部利用深层神经网络提取所述容纳的三维病变影像的特征,应用所述提取的三维病变影像,基于训练的深层神经网络诊断脑中风。
5.根据权利要求4所述的脑中风诊断以及预后预测系统,其特征在于,
所述深层神经网络包括三维卷积神经网络。
6.根据权利要求1所述的脑中风诊断以及预后预测系统,其特征在于,
所述多个影像是MRI影像。
7.根据权利要求6所述的脑中风诊断以及预后预测系统,其特征在于,
所述MRI影像包括:扩散加权成像、液体衰减反转恢复影像、梯度回波影像、T2加权像。
8.一种脑中风诊断以及预后预测方法,根据脑中风诊断以及预后预测方法,其特征在于,包括如下的步骤:
获取包括人脑的至少一部分的多个影像;
以标准脑为基准排列所述多个影像;
从所述多个影像分别检出病变区域,并且映射于所述检出的病变区域生成一个映射影像;
以缩放所述映射影像整合于标准脑影像,并且对所述映射影像执行影像校正;
以将所述映射影像容纳于三维数据空间生成三维病变影像;以及
基于所述三维病变影像诊断脑中风,
其中,所述诊断脑中风的步骤包括:
利用深层神经网络提取容纳于所述三维数据空间的三维病变影像的特征的步骤;
以所述提取的三维病变影像的特征为基础分类脑中风原因的步骤;
分类所述诊断的脑中风的重症程度的步骤;
从所述病变影像预测三周内的病危风险的步骤;以及
预测规定时间之后的患者状态的步骤。
9.根据权利要求8所述的脑中风诊断以及预后预测方法,其特征在于,
所述生成一个映射影像的步骤包括基于所述整合的位置信息,将所述映射的病变的位置整合于所述标准脑影像的步骤。
10.根据权利要求8所述的脑中风诊断以及预后预测方法,其特征在于,还包括如下的步骤:
在生成所述三维病变影像之前对所述病变影像根据有无病变,将所述病变影像的像素信息二进制化,并调节所述病变影像的大小。
11.根据权利要求8所述的脑中风诊断以及预后预测方法,其特征在于,
所述诊断脑中风的步骤包括利用三维卷积神经网络提取所述容纳的三维病变影像的信息的特征的步骤。
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