CN105103163A - 血管内图像中的多模态分割 - Google Patents

血管内图像中的多模态分割 Download PDF

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CN105103163A
CN105103163A CN201480012790.3A CN201480012790A CN105103163A CN 105103163 A CN105103163 A CN 105103163A CN 201480012790 A CN201480012790 A CN 201480012790A CN 105103163 A CN105103163 A CN 105103163A
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B·斯图姆
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Philips Image Guided Therapy Corp
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Volcano Corp
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/48—Other medical applications
    • A61B5/4887—Locating particular structures in or on the body
    • A61B5/489—Blood vessels
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/0035—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room adapted for acquisition of images from more than one imaging mode, e.g. combining MRI and optical tomography
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/0033—Features or image-related aspects of imaging apparatus classified in A61B5/00, e.g. for MRI, optical tomography or impedance tomography apparatus; arrangements of imaging apparatus in a room
    • A61B5/0037—Performing a preliminary scan, e.g. a prescan for identifying a region of interest
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/06—Devices, other than using radiation, for detecting or locating foreign bodies ; determining position of probes within or on the body of the patient
    • A61B5/061—Determining position of a probe within the body employing means separate from the probe, e.g. sensing internal probe position employing impedance electrodes on the surface of the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4416—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to combined acquisition of different diagnostic modalities, e.g. combination of ultrasound and X-ray acquisitions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/25—Fusion techniques
    • G06F18/253—Fusion techniques of extracted features
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/36—Image-producing devices or illumination devices not otherwise provided for
    • A61B2090/364—Correlation of different images or relation of image positions in respect to the body
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B90/00—Instruments, implements or accessories specially adapted for surgery or diagnosis and not covered by any of the groups A61B1/00 - A61B50/00, e.g. for luxation treatment or for protecting wound edges
    • A61B90/39—Markers, e.g. radio-opaque or breast lesions markers
    • A61B2090/3966—Radiopaque markers visible in an X-ray image
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B8/00—Diagnosis using ultrasonic, sonic or infrasonic waves
    • A61B8/44—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device
    • A61B8/4444—Constructional features of the ultrasonic, sonic or infrasonic diagnostic device related to the probe
    • A61B8/445—Details of catheter construction
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/03—Recognition of patterns in medical or anatomical images

Abstract

本发明提供了一种通过以下方式来检测脉管图像中的感兴趣特征的方法：接收来自第一模态的信息，并且将来自所述第一模态的信息变换到第一坐标空间中；接收来自第二模态的信息，并且将来自所述第二模态的信息变换到第二坐标空间中。将所述第一坐标空间与所述第二坐标空间对齐，以将来自所述第一模态和所述第二模态的信息组合为组合的数据集。所述方法还能够涉及基于所述组合的数据集，来检测脉管图像中的感兴趣特征。

Description

血管内图像中的多模态分割
相关申请的交叉引用
本申请主张2013年3月7日递交的美国临时申请序列号61/774154的利益和优先权，通过引用将其内容并入本文。
技术领域
本发明总体上涉及检测脉管图像中的感兴趣特征。
背景技术
心血管疾病经常起因于动脉粥样物在血管腔内的沉积，尤其是冠状动脉的动脉腔和其他脉管系统的内壁上的积累，从而导致已知为动脉粥样硬化的状况。这些沉积能够具有广泛变化性质，其中，一些沉积是相对软的，并且其他的是纤维状和/或钙化的。在后者的情况下，沉积经常被称为斑块。这些沉积能够限制血液流动，在更严重的情况下导致心肌梗死。
心血管疾病的评估和随后的处置常常利用各种成像模态来对血管系统的内部进行成像。这些成像模态能够包括X射线透视成像，光学相干断层摄影(OCT)成像，血管内超声(IVUS)成像以及虚拟组织学血管内(VH-IVUS)成像以及其他。X射线透视法使用X射线来获得结构或对象的实时移动图像。OCT使用反射光来创建深度分辨图像。IVUS利用超声回波来采集血管和周围区的图像。VH-IVUS是产生动脉血管的颜色编码图的成像技术，其中，不同组织组分被分派不同的颜色。
尽管所有这些模态以其自身方式是有用的，但是它们也具有它们的局限，尤其是当检测特定感兴趣特征时。例如，常规灰度IVUS不能够用于对放置在血管内部的支架进行成像，而没有值得考虑的困难。此外，常规IVUS也不能够容易地对腔的边界进行成像，这是由于在血管中的血液的存在。这些成像模态的局限能够阻碍针对合适地诊断和处置心血管疾病的努力。
发明内容
本发明提供了一种用于基于从多个成像模态导出的共配准的数据集来检测脉管图像中的感兴趣特征的方法。不同于依赖于仅一个成像模态来检测感兴趣特征的常规成像技术，本发明使用来自多个成像模态的潜在地互补信息，并组合所提取的信息，以方便检测感兴趣特征。本发明然后出于分析目的来使用成像数据的共配准的集合。例如，感兴趣特征可以是心血管支架，这是难以使用常规灰度IVUS来检测的。在该范例中，本发明可以涉及使用常规灰度IVUS来获得包括并且围绕支架的区的图像。信息然后从图像被提取并且被变换为位置数据(即坐标集)。然后利用第二成像模态，例如VH-IVUS，来对相同的区进行成像。尽管在灰度IVUS中的支架难以检测，但是在VH-IVUS中的支架具有可以容易地识别的独特的外观。凭借VH分析，VH-IVUS数据集和灰度IVUS数据集被空间地共配准，从而提供共配准的位置数据的组合的集合。从VH-IVUS提取的特征能够与从灰度IVUS数据提取的特征组合，从而然后训练搜索算法，所述搜索算法能够用于回来识别常规灰度IVUS中的支架。该多模态检测方法的主要益处是利用从互补成像模态获得的额外的信息。该额外的信息可以改进或促进脉管集中的感兴趣特征的检测。
任何成像模态可用于实践本发明，包括灰度IVUS、VH-IVUS、OCT、MRI、X线血管造影以及光声成像。此外，本发明能够被应用于方便使用前述成像模态来检测任何感兴趣特征。感兴趣特征可以是生物学的，例如，血管的边界或壁。感兴趣特征也可以是非生物学的。非生物学感兴趣特征可以包括已被插入到身体腔中的医学设备，例如，支架、球囊或导管。
本发明还涵盖用于实践上述方法的系统。本发明的特定方面尤其适于计算机实施，例如，来自各种成像模态的信息的接收和变换，以及将来自多个模态的位置数据对齐到组合的数据集中。因此，本发明的系统可以包括用于运行本发明的方法的计算机和处理器。
附图说明
图1示出了用于实施血管内图像配准的系统。
图2描绘了说明性血管造影图像。
图3示出了安装在导管之上的射线不透明标记物的X射线透视图像。
图4描绘了在横断面IVUS图像旁边的说明性增强放射图像。
图5图示了根据特定实施例的用于实践本发明的方法的系统。
图6描绘了用于识别感兴趣特征的示范性方法。
图7描绘了用于训练用在实践根据本发明的方法中的搜索算法的示范性方法。
图8图示了根据特定实施例的根据单幅图像数据集的基于特征的分割。
图9图示了根据特定实施例的根据多幅/多模态图像数据集的基于特征的分割。
具体实施方式
本发明提供了用于基于从多个成像模态导出的共配准的数据集来检测脉管图像中的感兴趣特征的方法。本发明利用来自多个成像模态的潜在地互补的信息，并且组合从模态提取的信息，以方便检测期望的感兴趣特征。在特定方面中，本发明可以涉及从第一成像模态接收信息，并将信息从第一模态变换到第一坐标空间中，即变换为位置数据或坐标的集合。本发明还可以涉及从第二成像模态接收信息，并将信息从第二模态变换到第二坐标空间中。本发明还可以涉及将第一坐标空间与二坐标空间对齐，从而将来自第一模态和二模态的信息组合为组合的数据集。本发明然后应用来自组合的数据集的信息，来搜索选定的模态中的感兴趣特征。例如，信息可以用于训练搜索算法来检测感兴趣特征。一般而言，本发明利用从共配准的数据集导出的信息来方便检测感兴趣特征。
来自多个成像模态的位置数据的对齐通常被称为共配准。共配准通常是指重新对齐图像(并且尤其是对齐或叠加来自从不同的模态的图像)的任何方法。共配准常常用于叠加结构图像和功能图像，以及将功能扫描链接到解剖扫描。来自多个成像模态的图像和位置数据的共配准是本领域中已知的。关于图像共配准的细节能够例如在美国专利No.8298147和8620055；以及U.S.Pub.2012/0155734中找到，所述专利中的每个通过引用被并入本文。
现在描述共配准的示范性方法，其使用X射线透视法和血管内超声来获得共配准的血管内数据集。然而，本发明涵盖任何以及全部成像模态，包括但不限于，血管内超声(IVUS)、光学相干断层摄影(OCT)、X射线血管造影、计算机断层摄影(CT)血管造影以及磁共振(MR)血管造影。
图1示出了对于血管造影或X射线透视图像和血管内超声图像的共配准而言有用的本发明的系统。放射和超声图像数据采集子系统一般都是本领域中公知的。关于放射图像数据，患者10被定位在血管造影台12上。血管造影台12被布置为提供足够的空间，来关于在台12上的患者10将血管造影/X射线透视单元C型臂14定位在操作位置中。由血管造影/X射线透视C型臂14采集的放射图像数据经由传送线缆16传到血管造影/X射线透视处理器18。血管造影/X射线透视处理器18将经由线缆16接收的所接收的放射图像数据转换为血管造影/X射线透视图像数据。血管造影/X射线透视(“放射”)图像数据初始存储在处理器18内。
关于与采集超声图像数据相关联的系统的部分，成像导管20，并且尤其是IVUS导管，被插入在患者10内，使得其远端，包括诊断探头22(尤其是IVUS探头)，在血管的期望的成像位置的附近。尽管没有具体在图1中被识别，但是定位在探头22附近的辐射不透明材料提供探头22在放射图像中的当前位置的记号。通过范例，诊断探头22生成超声波，接收表示邻近诊断探头22的区域的超声回声，并将超声回声转换为对应的电信号。对应的电信号沿成像导管20的长度被发送到近端连接器24。探头22的IVUS版本有各种配置，包括单个换能器元件布置和多个换能器元件布置。在多个换能器元件布置的情况下，换能器阵列可能被布置为：沿成像导管20的纵轴线性地，关于导管20的纵轴曲线地，在纵轴的周围环绕地，等。
导管20的近端连接器24通信地耦合到导管图像处理器26。导管图像处理器26将经由近端连接器24接收的信号转换为，例如，血管段的横截面图像。此外，导管图像处理器26生成对应于沿血管的长度获取的血管的切片的纵向横截面图像。由导管图像处理器26绘制的IVUS图像数据初始存储在处理器26内。
由诊断探头22采集的，并且由导管图像处理器26处理的诊断成像数据的类型根据本发明的备选实施例而变化。根据特定的备选实施例，诊断探头22被装备有一个或多个传感器(例如，多普勒和/或压力)，以提供血流动力学信息(例如，血流速度和压力)-也被称作为功能流测量。在这样的备选实施例中，由导管图像处理器26来处理功能流测量值。因此应注意，术语“图像”旨在被广泛解释为涵盖表示血管信息的各种方式，所述血管信息包括血压、血流速度/体积、血管截面组成、遍及血液的剪切应力、在血液/血管壁界面处的剪切应力等。在采集针对血管的特定部分的血流动力学数据的情况下，高效诊断依赖于对血管系统内的诊断探头22的当前位置进行可视化，同时观测指示心血管疾病的功能流度量的能力。血流动力学图像和放射图像的共配准方便患病血管的精确处置。备选地，代替于安装传感器的导管，传感器能够安装在导丝上，例如，在直径0.018”或更小的导丝上。因此，根据本发明的实施例，不仅有各种探头类型被使用，还有各种柔性细长构件(例如，导管、导丝等)被使用，这样的探头在远端处被安装到所述各种柔性细长构件。
共配准处理器30经由线32从导管图像处理器26接收IVUS图像数据，并且经由线34从放射图像处理器18接收放射图像数据。备选地，传感器与处理器之间的通信经由无线介质来执行。共配准处理器30绘制共配准图像，所述共配准图像包括从接接收的图像数据导出的放射图像帧和IVUS图像帧二者。根据本发明的实施例，记号(例如，辐射不透明标记物伪影)被提供在放射图像的对应于同时显示的IVUS图像数据的位置上。共配准处理器30初始缓冲经由线34从放射图像处理器18接收的在图像数据存储器40的第一部分36中的血管造影图像数据。此后，在插管术流程的过程期间，经由线32和34接收的IVUS和辐射不透明标记物图像数据分别存储在图像数据存储器40的第二部分38和第三部分42内。存储的图像数据的个体绘制的帧被适当地加标签(例如，时间戳、序列号等)以将IVUS图像帧与对应的放射(辐射不透明标记物)图像数据帧进行相关。在实施例中，其中，血流动力学数据被采集，而不是IVUS数据，血流动力学数据存储在第二部分38内。
此外，额外的标记物能够被放置在血管造影/X射线透视成像设备的视场内的患者的表面上或患者的附近内。这些标记物的位置然后用于在血管造影图像上将辐射不透明标记物伪影定位在准确位置中。
共配准处理器30根据先前存储在图像数据存储器40的第一部分36、第二部分38和第三部分42内的数据来绘制共配准图像。通过范例，特定IVUS图像帧/切片从第二部分38被选择。共配准处理器30识别对应于来自第二部分38的选定的IVUS图像数据的在第三部分42内的X射线透视图像数据。此后，共配准处理器30将来自第三部分42的X射线透视图像数据叠加在从第一部分36检索的血管造影图像帧上。此后，共配准的放射和IVUS图像帧在图形显示设备50上同时彼此并排地被显示。驱动显示设备50的共配准的图像数据帧也存储在长期存储设备60内，以用于在区别于采集存储在图像数据存储器40中的放射和IVUS图像数据的流程的会话中的之后的查看。
尽管图1中未示出，但是拉回设备被并入，其以受控的/测量的方式从患者拉出导管20。这样的设备是本领域中公知的。这样的设备的并入使得计算当X射线透视法未激活时在时间中的点处在视场内的探头22的当前位置容易。
图2呈现了采取期望的投影(患者/血管取向)以及放大率的血管造影“路线图”图像200，如由血管造影/X射线透视处理器18捕获的。通过范例，图200由在追踪去往患者的血管系统内的感兴趣区域的IVUS导管之前执行的血管造影流程被初始捕获。在没有导管20在血管中的情况下执行血管造影流程提供最大对比流，更好的血管填充，以及因此更好的整体血管造影图像。因此，诸如侧分支210和其他血管界标的侧分支在显示在图形显示设备50上的共配准图像的放射图像部分上能够清晰地被显示及看到。
图3示出了在X射线透视图像中可见的导管辐射不透明标记物300。导管20被追踪到它的开始位置(例如，其中IVUS拉回流程开始的位置)。通常，导管20在先前前进的导丝上(未示出)被追踪。此后，获得X射线透视图像。在如图3所示的图像中，导管辐射不透明标记物300被看到，但血管腔不是，这是由于缺乏对比流。然而，存在于血管造影图像和X射线透视图像二者上的定位标记物的集合使得能够将标记物图像适当定位(叠加)在先前获得的血管造影图像内。在查看了本文的技术之后，在血管造影图像的视场内适当定位辐射不透明标记物图像的其他方式对于本领域的技术人员而言将是已知的。此外，标记物伪影能够在叠加的图像帧上被自动调节(尺寸和位置二者)，以对应于换能器的近似位置。
图4呈现了示范性共配准图像，其产生于在血管造影图像上交叠或叠加辐射不透明标记物伪影。示范性共配准显示界面401(包括相关的放射和超声图像)描绘了血管的选定的横截面IVUS图像400。放射图像410同时在IVUS图像400的旁边显示在显示器50上。放射影像410包括标记物伪影420，所述标记物伪影是根据由X射线透视图像帧绘制的放射图像数据生成的，所述标记物伪影被叠加在根据存储器40的第一部分36绘制的血管造影背景上。X射线透视图像帧对应于诊断探头22在正在观测中的血管内的当前位置。对在血管造影图像和X射线透视图像二者中表示的视场的精确匹配(即，两幅图像的精确投影和放大率)允许在图4中显示的共配准的图像的正确方格中的对对应于显示的IVUS图像400的IVUS探头的当前位置的识别。在美国专利No.7930014中可以发现对图像共配准的论述，通过引用将其并入本文。
一旦共配准的数据集例如通过使用上述方法被获得，然后共配准的数据集就能够被应用于使得检测给定的模态中的感兴趣特征容易。在特定实施例中，这可以包括使用共配准数据集来训练用于检测给定的模态中的感兴趣特征的搜索算法。例如，感兴趣特征可以是支架。可以利用两个成像模态，例如IVUS和VH-IVUS，来对支架和周围血管系统进行成像。特征从二者被提取并且被对齐，以获得共配准的数据集。该组合的数据集然后能够用于训练搜索算法来检测常规灰度IVUS中的支架，在所述常规灰度IVUS中，支架的检测常常是有问题的。现在将描述用于训练搜索算法的合适的方法。
在特定方面中，通过使用已在共配准的血管内数据集上训练的搜索算法，来在成像模态中识别感兴趣的特征。如上所述，共配准的血管内数据集包括来自多个成像模态的关于感兴趣特征，例如血管壁的信息。基于提供在组合的数据集中的信息，搜索算法能够识别在给定的成像模态中的感兴趣特征。所述算法赋予特定因子或参数，以便基于从多个成像模态积累的位置和其他数据，进行综合性评估并且识别感兴趣特征。一般而言，所述过程涉及利用第一成像模态获得图像，从所述图像提取感兴趣特征，并且将所述特征变换为位置数据(即，坐标的集合)。所述过程还涉及利用第二成像模态获得区的至少第二图像，提取感兴趣特征，并再次将特征变换为位置数据。来自第一和第二成像模态的位置数据然后经由共配准组合为一个数据集，并且组合的数据集用于训练搜索算法，所述搜索算法被配置为检测在给定的成像模态中的感兴趣特征。
算法的训练可以包括迭代步骤的系列，其中，每个连续的步骤评估与在循环的先前步骤中提交的全部数据(先前的成像模态)和关于感兴趣特征的相关信息组合的每个新的数据(即，从一个额外的成像模态提供的)，直到针对分析输入或者提交的全部测试数据(即，位置数据)被综合评估为止。在完成最后步骤之后，分析功能终止，并在分析功能完成之后形成搜索结果。本发明还预期了基于从成像模态接收的新信息的对位置数据的修改或更新，这被包括为算法的部分，因为这样的数据变得可用，并且将服务于改进搜索的准确性。
本发明的搜索算法可以实现在任何合适的应用中，例如，能够方便其使用的计算机程序或代码中。实现应用的算法或应用可以存储在计算机的内部或外部硬盘驱动器、便携式驱动器或磁盘、服务器，临时或永久性的存储器设备，或使得算法的使用和/或根据其使用导出的结果的使用方便的任何其他存储设备。所述算法或应用优选地与使得预测性分析容易的至少一个处理设备通信，例如，计算机或网络处理器。所述算法或相关的应用可以局部地(例如，在单个的或联网的计算机上)或远程地(例如经由因特网或者经由以太网的基于网站的网络)被访问。
可以经由对任何合适的装备的使用来方便对算法或应用的该访问，所述任何合适的装备包括但不限于，电脑、因特网器具、电话设备、无线设备等。对算法、实现算法的应用或通过使用算法获得的结果的访问，可以是被保护的或被限制于一般访问或者经由密码、加密、生物特征激活或语音激活，或其他合适的保护措施使用的。
如本发明预期的，上述函数和实施例能够使用软件、硬件、固件、硬连线或这些的任何组合来实施。实施函数的特征也能够物理定位在变化位置处，包括被分布，使得函数的部分被实施在不同的物理位置处。
如本领域的技术人员将认识到，为必要的或者最适于执行本发明的方法的，本发明的计算机系统或机器包括一个或多个处理器(例如，中央处理单元(CPU)、图形处理单元(GPU)或二者)，主存储器和静态存储器，其经由总线彼此通信。本发明的系统包括计算机和处理器以及计算机可读存储介质指令，所述计算机可读存储介质指令当被运行时，令计算机从第一成像模态接收信息，并将信息变换到第一坐标空间中，从第二成像模态接收信息，并且将信息变换到第二坐标空间中，并将第一坐标空间与第二坐标空间对齐，从而将来自第一模态和第二模态的信息组合为组合的数据集。所述指令还可以令计算机基于组合的数据集，来检测在第三成像模态中的感兴趣特征。
图5图解示出了根据本发明的实施例的系统100。系统100优选地包括计算机249(例如，膝上电脑、台式电脑、平板电脑或智能手机)。计算机249可以被配置为跨过网络109进行通信。计算机249包括一个或多个处理器159和存储器163以及输入/输出机构154。在本发明方法采用客户端/服务器架构时，本发明的方法的步骤可以使用服务器113来执行，所述服务器包括处理器121和存储器129中的一个或多个，其能够获得数据、指令等，或经由接口模块125提供结果或提供作为文件117的结果。服务器113可通过计算机249或端子267衔接在网络109上，或者服务器113可以直接连接到端子167，所述端子包括一个或多个处理器175和存储器179，以及输入/输出机构171。
针对I/O154或171中的任一项，根据本发明的系统100或机器还可以包括视频显示单元(例如，液晶显示器(LCD)或阴极射线管(CRT))。根据本发明的计算机系统或机器还能够包括字母数字输入设备(例如，键盘)、光标控制设备(例如，鼠标)、磁盘驱动单元、信号生成设备(例如，扬声器)、触摸屏、加速度计、麦克风、蜂窝射频天线以及网络接口设备，所述网络接口设备例如能够是网络接口卡(NIC)、Wi-Fi卡或蜂窝调制解调器。
根据本发明的存储器163、179或129能够包括机器可读的介质，实现本文描述的方法或函数中的任何一个或多个的一个或多个指令集(例如，软件)存储在所述机器可读介质上。在优选实施例中，本发明的计算机系统包括一个或多个存储器设备，所述一个或多个存储器设备是有形非暂态存储器。所述软件还可以完全或至少部分地驻存于主存储器内和/或处理器内，在它的通过计算机系统的运行期间，所述主存储器和处理器也构成机器可读介质。所述软件还可以经由网络接口设备跨过网络被发送或接收。
图6呈现了本发明的方法的步骤。应将理解，本文描述的方法，以及本文所公开的系统和方法的任何部分，能够由计算机来实施，所述计算机包括上面描述的设备。优选地，每个步骤由处理器或连接的医学成像设备来执行。使用给定的模态获得其中感兴趣特征需要识别的图像201。所述模态例如可以是灰度IVUS，并且感兴趣特征是支架，其难以利用灰度IVUS来检测。从成像模态提取关于感兴趣特征的信息。该特征被转换为包括关于感兴趣特征的位置数据的特征向量。该数据然后被输入到计算机的中央处理单元(中央处理器)中202。CPU被耦合到用于存储指令的存储设备或存储器，所述指令用于实施本发明的方法，例如搜索算法。指令当由CPU运行时，令CPU识别在成像模态中的选定的感兴趣特征。CPU通过将来自成像模态的数据输入到在共配准的数据集上训练的算法中来提供该确定，所述共配准的数据集是针对关于已知的感兴趣特征的位置数据从多个成像模态导出的203。共配准的数据集可以局部地存储在计算机内，例如在计算机存储器内。备选地，共配准的数据集可以存储在远离计算机的位置中，例如在服务器中。在该情况下，计算机跨过网络进行通信，以访问共配准的数据集。然后，CPU基于输入到算法204中的数据，来识别在成像模态中的感兴趣特征。
在准备从多个成像模态导出的共配准的位置数据集之后，公知的技术，例如互相关、主成分分析(PCA)、因子旋转、逻辑回归(LogReg)、线性判别分析(LDA)、特征基因线性判别分析(ELDA)、支持向量机(SVM)、随机森林(RF)、递归分割树(RPART)、相关决策树分类技术、收缩质心(SC)、步进AIC(stepAIC)、第k最近邻、推进(Boosting)、决策树、神经网络、贝叶斯网络、支持向量机、以及隐马尔可夫模型、线性回归或分类算法、非线性回归或分类算法、变异分析(ANOVA)、分层分析或聚类算法；使用决策树的分层算法；基于核的机器算法，例如核部分最小二乘算法、核匹配追踪算法、核费舍尔判别分析算法或者核主成分分析算法，或者其他数学和统计方法能够用于开发搜索算法。从多个成像模态导出的共配准的位置数据集用于训练所述算法。为了识别成像模态中的选定的感兴趣特征，关于感兴趣特征的位置数据被从多个成像模态获得，并组合到共配准的位置数据集中，所述共配准的位置数据集然后被用作输入数据(到搜索算法中的输入，所述搜索算法被拟合到从个体的选定的群体获得的共配准的位置数据)。任何公式或算法可以用于基于从各种成像模态提取的感兴趣特征来组合位置数据。
尽管此处描述了各种优选方法，但是除了本文所提及的并且在上面的定义中的这些之外，若干其他模型和方法类型对于本领域中的技术人员而言是公知的。所使用的实际模型类型或方法本身可以基于其在训练群体中的结果的性能和诊断准确性特性，来从潜在的模型的领域被选择。方法本身的细节可以公共地从在相关训练群体中的选定的参数结果导出。除其他用途之外，这样的方法可以旨在将从一个或多个选定的参数输入导出的特征空间映射到对象类的集合，以使用贝叶斯方法导出风险的概率函数的估计，或者以估计类条件概率，然后使用贝叶斯的规则产生类概率函数，如在先前的情况下。
优选公式包括广泛类别的统计分类算法，并且尤其是对判别分析的使用。判别分析的目标是根据先前识别的特征集来预测类成员。在线性判别分析(LDA)的情况下，特征的线性组合被识别，从而最大化通过一些准则的在组之间的分离。能够使用具有不同阈值的基于特征基因的方法(ELDA)或者基于多变量方差分析(MANOVA)的步进算法，来针对LDA识别特征。向前、向后以及逐步算法能够被执行，其基于Hotelling-Lawley统计来最小化没有分离的概率。
基于特征基因的线性判别分析(ELDA)是在Shen等的Eigengene-basedlineardiscriminantmodelfortumorclassificationusinggeneexpressionmicroarraydata，Bioinformatics，22(21)、第2635-2642页(2006)中开发和报告的特征选择技术。所述公式使用修改的特征分析来选择在多变量框架中的特征(例如，参数)，以识别与最重要的特征向量相关联的特征。“重要”被定义为这些特征向量解释在相对于特定阈值试图要被分类的样本中间的在差异中的最多变化。
支持向量机(SVM)是试图找到分离两个类的超平面的分类公式。这个超平面包含支持向量，精确地是远离超平面的边缘距离的数据点。在没有分离超平面存在于数据的当前维中的可能情况下，通过采取原始变量的非线性函数，通过将数据投影到更大的维中来极大地扩展维度(VenablesandRipley，2002，ModernAppliedStatisticswithS4Ed，SpringerVerlag)。尽管不是要求的，针对SVM的特征的滤波常常改进预测。能够使用非参数kruskal-wallis(KW)检验来识别针对支持向量机的特征(例如，参数/生物标记物)，以选择最好的单变量特征。随机森林或递归分割(REPART，Breiman等人，1984)也能够独立地或组合被使用以识别最重要的参数组合。随机森林和递归分割在以下文章中进行了讨论：Strobl等人的AnIntroductiontoRecursivePartitioning:Rationale,ApplicationandCharacteristicsofClassificationandRegressionTrees,BaggingandRandomForests，PsycholMethods，14(4)、第323-348页(2009)；Breiman的Randomforests，MachineLearning，45，第5-32页(2001)；Breiman的ClassificationandRegressionTrees,BocaRaton:Chapman&Hall/CRC(1984)；美国专利8600917；以及美国专利8187830，通过引用并入本文。KW和RF二者需要多个特征从总体被选定。RPART使用可用参数的子集来创建单个分类树。
可以使用其他的公式，以便在它们的提供到预测性公式之前，将个体选定的参数测量值的结果预先处理为更有价值的信息格式。最值得注意的是，参考成员的平均值等的，使用诸如对数函数或逻辑函数(Logisticfunction)的任一常见数学变换的而作为常规或者其他分布位置的对参数结果的归一化对本领域的技术人员而言是公知的。
除了潜在地被归一化的一个对象的个体参数值之外，针对全部对象或者对象的任何已知类的整体预测性公式其本身可以根据以下基于针对群体的成员的希望的流行率和平均参数值的调节而被重新校准或以其他方式被调节：在D'Agostino等人的ValidationoftheFraminghamcoronaryheartdiseasepredictionscore:resultsofamultipleethnicgroupinvestigation，JAMA，286、第180-187页(2001)中概述的技术或其他类似的归一化和重新校准技术。这样的调节统计可以通过将过往数据的记录提供到模型而连续地被捕获、改进和更新，所述记录可以是机器可读的或者其他方式的，所述调节统计或者通过存储的样本的回溯询问或者参考这样的参数和统计的历史研究偶然地被捕获、改进和更新。可以是公式重新校准或其他的调节的对象的额外的范例包括在研究中通过让步比的限制使用的统计(参见Pepe等人的Limitationsoftheoddsratioingaugingtheperformanceofadiagnostic,prognostic,orscreeningmarker，AmJEpidemiology，159(9)、第882-890页(2004))以及在风险预测中的受试者操作特性(ROC)曲线(参见Cook的Useandmisuseofreceiveroperatingcharacteristiccurveinriskprediction，Circulation，115(7)、第928-35页(2007)；Wang等人的Multiplebiomarkersforthepredictionoffirstmajorcardiovasculareventsanddeath，NEJM，355、第2631-2639页(2006)；以及Grand&Sabin的Analysisofbiomarkerdata:logs,odds,ratiosandROCcurves，CurrOpinHIVAIDS，5(6)、第473-479页(2010))。此外，分类器公式自身的数值结果可以通过对选定的感兴趣特征已经被识别和确认的实际正控制的它的参考，在后处理中被变换。
图7是表示用于模型开发的示范性方法300的流程图，所述示范性方法可以用于搜索感兴趣特征。方法300可以使用图5的范例计算系统环境100来被实施，并且将用于解释环境100的操作。然而，应认识到，方法300能够由不同于计算系统环境100的系统来实施。在方框301处，关于选定的感兴趣特征的共配准的位置数据集，如本文已经描述的，从数据存储设备被获得，所述数据存储设备例如是系统存储器129、内部或外部数据库或者其他计算机存储介质。可以通过各种手段，包括捕获图像中的感兴趣特征的各种成像模态，来初始导出共配准的数据集，其中，特征从成像模态被提取，并被转换为位置数据(即，坐标的集合)。
在方框302处，共配准的数据集如需要地被准备，以满足将用于训练搜索算法的模型或者分析的要求，如下面描述的。例如，数据集准备可以包括准备来自每个成像模态的位置数据，或者其选择子集。当必要时，各种数据准备方法可以用于在训练算法之前准备数据，所述各种数据准备方法例如是间隙填充技术(例如，最近邻插值或其他模式识别)、质量检查、使用各种公式的数据组合(例如，统计分类算法)、规范化和/或变换，例如用于改变数据的分布以满足模型的要求的对数函数(例如，基数10，自然对数等)。再次，所述特定的数据准备流程取决于一个或多个模型，所述一个或多个模型将使用共配准的数据集来被训练。用于各种不同模型类型的特定数据准备技术是已知的，并且不需要进一步描述。
在方框303处，特定提取的特征被变换为位置数据，所述位置数据用于选择随后被用在训练用于识别给定的成像模态中的感兴趣特征的搜索模型中的参数。对共配准的位置数据的使用可以涉及利用来自提供最可重复结果的共配准的集合的仅位置数据。数据集验证的范例可以包括，但不限于交叉验证和自举方法。根据参数选择，用于识别给定的成像模态中的感兴趣特征的最佳搜索模型可以被确定和选择。然而，应注意到，并不是所有的模型关于相同的数据集提供相同的结果。例如，不同的模型可以利用位置数据的不同方面并产生不同的结果，从而增加了优化的模型中的选定的位置数据的显著性。因此，多选择模型可以被选择并且与共配准的数据集，或共配准的数据集的子集一起被利用，以便识别用于识别给定的模态中的感兴趣特征的最优搜索模型。可以用于从共配准的数据集选择位置数据的特定的模型的范例，包括统计模型、算法等，在上面已被描述。
针对与数据集或其子集一起使用的每个选择模型，基于模型中的其统计显著性来选择位置数据。当被输入到每个模型中时，位置数据基于针对统计显著性的各种准则被选择，并且还可以涉及累积投票和加权。针对统计显著性的测试可以包括退出测试和方差分析(ANOVA)。所述模型可以包括分类模型(例如，LDA、逻辑回归、SVM、RF、树模型等)和生存模型(例如，COX)，上面已经描述了其的许多范例。
在方框304处，要用于识别感兴趣特征的搜索模型被选择、训练和验证。尤其是，领先的候选模型可以基于一个或多个性能准则被选择，上面已经描述了所述性能准则的范例。例如，根据利用各种模型来使用数据集或数据子集，不仅模型用于确定统计显着参数，而且结果可以用于选择与参数一起的最优模型。这样一来，用于评估风险的评估模型可以包括用作选择模型的这些中的一个，包括分类模型和生存模型。模型标记物(包括标记物子集)的组合，可以在子集和个体数据集中被比较和验证。比较和验证可以被重复多次，以训练和验证模型，并且选择适当的模型，所述适当的模型然后被用作用于评估糖尿病状况的风险的评估模型。
现在将参考本发明在特定的情形中的应用。应理解，这些实施例不是限制性的。如下面的范例所图示的，本发明涉及对在从多个数据集或成像模态导出的脉管图像中的感兴趣特征的检测，所述感兴趣特征例如是血管边界和支架支柱。本发明的方法潜在地利用来自多个成像模态的互补信息，并且组合从每幅图像提取的信息，来检测目标特征。
图像分割是将数字图像划分为多个片段(像素的集合，也被称为超像素)的过程。分割的目标是将图像的表示简化和/或改变为更有意义并且更容易分析之物。图像分割通常用于在图像中定位对象和边界(线、曲线等)。分割的一种方法是计算并且提取来自图像数据的特征。特征能够被组合为特征向量(例如，被变换为位置数据)，所述特征向量然后能够与已知的特征向量(即，金标准信息)进行匹配或通过经训练的分类器(例如，神经网络或其他算法)来运行以将图像数据分类到预先确定的类中。
图8图解示出了将数字图像划分为多个片段的过程。如图8所示，并且图像被获得801，并且特征从图像被提取802。特征然后被组合为特征向量803(例如，被变换为位置数据)，所述特征向量然后能够用于训练搜索算法804，以识别在给定的模态中的选定的感兴趣特征805。给定从多个成像模态导出的空间上共配准的血管内图像的集合，然而，特征能够被计算并且从多幅图像被提取，并且被组合为单个特征向量。如果多模态源，基于多个成像模态的共配准的数据集能够提供互补信息，所得到的特征向量包含比当从单个源提取时更多的信息。因此，在该共配准的数据集上训练的搜索算法应当提供更准确的结果。
图9图示了由多个成像模态捕获的图像901，并给出了图像分割过程的范例。如上面指示的，能够根据本发明使用任何成像模态。例如，在图9中描绘的成像模态包括IVUS、X射线血管造影以及OCT。在图像被获得901之后，特征从图像被提取902，并且被变换为位置数据903。所述位置数据然后被组合并且被对齐到共配准的数据集904中。该共配准的数据集904然后用于训练搜索算法905，所述搜索算法能够用于识别给定的模态906中的感兴趣特征。本发明的方法能够容易地非限制地被应用于情形中。
VH-IVUS(虚拟组织学-血管内超声)和灰度IVUS(血管内超声)：支架已知为难以使用常规灰度超声来检测。本发明的方法然而能够通过组合IVUS和VH-IVUS的互补成像模态来检测非生物学感兴趣特征，以提供更多信息。借助于VH分析，VH-IVUS数据和灰度IVUS数据已经被空间地共配准。VH-IVUS中的支架具有不同外观，其能够容易地在IVUS图像中被识别。该信息能够用于初始化用于灰度IVUS数据集中的支架检测的搜索方法，或者与从灰度IVUS数据集提取的特征组合为支架检测算法的部分。
灰度IVUS和OCT支架检测：如上面指出的，支架是难以单独使用灰度IVUS来检测的。在该范例中，本发明的方法利用IVUS数据和OCT数据的共配准的集合来辅助支架检测。给定空间共配准的灰度IVUS数据集和OCT数据集的集合，特征能够从每个模态被提取并被组合为单个特征向量，如上面描述的。该范例能够被扩展到涵盖VH-IVUS、灰度IVUS和OCT的方法。
灰度IVUS和OCT血管和腔边界检测：该范例与上面的相同，然而，感兴趣特征现在是生物学的而不是非生物学的。成像模态的该特殊组合能够是显著有利的，因为单独利用灰度IVUS的对腔边界的检测能够是非常具有挑战性的，这是由于血液的存在。另一方面，OCT采集发红的血管中的图像数据，这使得对腔边界成像容易。如果感兴趣特征是血管边界，那么OCT数据变得较不显著，除了血管边界总是在腔的外部并且OCT数据适于识别腔的事实。
应理解，这些范例不是限制性的，并且，本发明包括所有成像模态和所有感兴趣特征，无论它们是生物学的或者非生物学的。例如，该脉管多模态方法也能够被应用于与IVUS和/或OCT数据集共配准的X射线血管造影和/或CT血管造影数据。此外，本发明不限于IVUS和OCT作为仅血管内成像模态。其他合适的血管内成像模态包括MRT和光声成像。本文所描述的多模态方法的主要益处是利用当可利用时从互补成像模态导出的额外的信息。该额外的信息当根据所提供的方法被使用时，改进或促进对在脉管数据集中的感兴趣特征的检测。
通过引用并入
在本公开内容中已经参考并且引用了其他文件，例如专利、专利申请、专利发表、期刊、书籍、论文、网络内容。所有这样的文件在此出于全部目的通过引用被整体并入本文。
等价方案
本发明可以以其他特定形式来体现，而不背离本发明的精神或实质特性。因此，上述的实施例要被视为在全部方面中是说明性的而非限制在本文中所描述的发明。本发明的范围因此由权利要求书而非由上述描述来指示，并且因此进入权利要求书的等价方案的意义和范围内的全部改变旨在被涵盖于本发明中。

Claims (20)

1.一种用于检测脉管图像中的感兴趣特征的方法，所述方法包括：
接收来自第一成像模态的信息，并且将来自所述第一成像模态的信息变换到第一坐标空间中；
接收来自第二成像模态的信息，并且将来自所述第二成像模态的信息变换到第二坐标空间中；
将所述第一坐标空间与所述第二坐标空间对齐，从而将来自所述第一模态和所述第二模态的信息组合为组合的数据集；并且
基于所述组合的数据集，来检测脉管图像中的感兴趣特征。
2.根据权利要求1所述的方法，其中，所述第一成像模态和所述第二成像模态中的成像模态是从包括以下的组中选择的：血管内超声(IVUS)、虚拟组织学血管内超声(VH-IVUS)、光学相干断层摄影(OCT)、X射线血管造影、以及磁共振成像。
3.根据权利要求1所述的方法，其中，所述第一模态不同于所述第二成像模态。
4.根据权利要求1所述的方法，其中，接收信息包括接收关于所述感兴趣特征的信息。
5.根据权利要求1所述的方法，其中，所述感兴趣特征包括生物学感兴趣特征。
6.根据权利要求5所述的方法，其中，所述生物学感兴趣特征是血管或腔壁。
7.根据权利要求1所述的方法，其中，所述感兴趣特征包括非生物学感兴趣特征。
8.根据权利要求7所述的方法，其中，所述非生物学感兴趣特征是支架。
9.根据权利要求1所述的方法，其中，检测所述感兴趣特征包括基于所述组合的数据集来初始化针对所述感兴趣特征的搜索。
10.根据权利要求1所述的方法，其中，检测所述感兴趣特征包括基于所述组合的数据集来训练用于检测所述感兴趣特征的算法。
11.一种用于确定在治疗流程之后的改进程度的系统，所述系统包括：
处理器；以及
计算机可读存储介质，其包括指令，所述指令当被运行时令所述系统：
接收来自第一成像模态的信息，并且将来自所述第一成像模态的信息变换到第一坐标空间中；
接收来自第二成像模态的信息，并且将来自所述第二成像模态的信息变换到第二坐标空间中；
将所述第一坐标空间与所述第二坐标空间对齐，从而将来自所述第一模态和所述第二模态的信息组合为组合的数据集；并且
基于所述组合的数据集，来检测脉管图像中的感兴趣特征。
12.根据权利要求11所述的方法，其中，所述第一成像模态和所述第二成像模态中的成像模态是从包括以下的组中选择的：血管内超声(IVUS)、虚拟组织学血管内超声(VH-IVUS)、光学相干断层摄影(OCT)、X射线血管造影、以及磁共振成像。
13.根据权利要求11所述的方法，其中，所述第一模态不同于所述第二成像模态。
14.根据权利要求11所述的方法，其中，接收信息包括接收关于所述感兴趣特征的信息。
15.根据权利要求11所述的方法，其中，所述感兴趣特征包括生物学感兴趣特征。
16.根据权利要求15所述的方法，其中，所述生物学感兴趣特征是血管或腔壁。
17.根据权利要求11所述的方法，其中，所述感兴趣特征包括非生物学感兴趣特征。
18.根据权利要求17所述的方法，其中，所述非生物学感兴趣特征是支架。
19.根据权利要求11所述的方法，其中，检测所述感兴趣特征包括基于所述组合的数据集来初始化针对所述感兴趣特征的搜索。
20.根据权利要求11所述的方法，其中，检测所述感兴趣特征包括基于所述组合的数据集来训练用于检测所述感兴趣特征的算法。
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CN109091167A (zh) * 2018-06-29 2018-12-28 东南大学 冠状动脉粥样硬化斑块增长的预测方法
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