English:
[0001] Traumatic Brain Injury (TBI) is one of the most common causes of long-term disability. Abnormality of several sub¬cortical structures such as, for example, the corpus callosum, hippocampus, cerebellum, thalamus and caudate, have been associated with TBI. Thus, it is important to identify the neuropathology in individuals with TBI in 3D. However, methodological challenges have hampered previous studies from providing a clear pattern of structural atrophy following TBI .
[0005] Fig. 1 shows a schematic diagram of a system according to an exemplary embodiment.
[0006] Fig. 2 shows a flow diagram of a method according to an exemplary embodiment .
[0007] Fig. 3 shows a screenshot of a deformable brain model initialized in a volumetric image displayed on a GUI.
[0008] Fig. 4 shows a screenshot of the deformable brain model of Fig. 3 after it has been adapted to the volumetric image .
[0009] The exemplary embodiments may be further understood with reference to the following description and the appended drawings, wherein like elements are referred to with the same reference numerals. The exemplary embodiments relate to a system and method for segmenting brain structures. In particular, the exemplary embodiments generate a deformable model of the brain structure, which may be adapted to a volumetric image such as an MRI . It will be understood by those of skill in the art, however, that although the exemplary embodiments specifically describe segmenting brain structures, the system and method of the present invention may be used to segment any anatomical, 3-dimensional structure in a volumetric image such as, for example, an MRI and/or an ultrasound image.
[0010] As shown in Fig. 1, a system 100 according to an exemplary embodiment segments a 3D brain structure such as, for example, the corpus callosum, hippocampus, cerebellum, thalamus and caudate, of a volumetric image such as an MRI or an ultrasound image. The system 100 comprises a processor 102 that is capable of adapting a deformable model of the brain structure based on features of the structure in the image. The deformable model is selected from a database of models stored in a memory 108. A graphical user interface 104 is utilized to input user preferences to determine a volume of the brain structure, display a deformation of the brain structure, view a particular portion of the brain structure, etc. Inputs associated with the graphical user interface are entered via, for example, a mouse, a touch display and/or a keyboard. The segmentation of the brain structure, the volumetric image and user options of the graphical user interface 104 are displayed on a display 106. The memory 108 may be any known type of computer-readable storage medium. It will be understood by those of skill in the art that the system 100 is, for example, a personal computer, a server or any other processing arrangement.
[0011] Fig. 2 shows a method 200 according to an exemplary embodiment, in which the system 100 segments a brain structure to identify deformations in the brain structure. The method 200 includes selecting a deformable model of a brain structure of interest from a database of structure models stored in the memory 108, in a step 210. In an exemplary embodiment, the deformable model is automatically selected by the processor 102 by comparing features of the brain structure of interest in the volumetric image to the structure models in the database. In another exemplary embodiment, the deformable model is manually selected by the user browsing through the database to identify the deformable model that most closely resembles the brain structure of interest. The database of structure models may include structure models from brain structure studies and/or segmentation results from previous patients.
[0012] In a step 220, the deformable model is displayed on the display 106, as shown in Fig. 3. The deformable model is be displayed as a new image and/or displayed over the volumetric image. The deformable model is formed of a surface mesh including a plurality of triangularly shaped polygons, each triangularly shaped polygon further including three vertices and edges. It will be understood by those of skill in the art, however, that the surface mesh may include polygons of other shapes. The deformable model is positioned such that the vertices of the deformable model are positioned as closely as possible to a boundary of the structure of interest. In a step 230, each of the triangular polygons is assigned an optimal boundary detection function. The optimal boundary detection function detects feature points along a boundary of the structure of interest so that each of the triangular polygons is associated with a feature point, in a step 240. The feature points may be associated with centers of each of the triangular polygons. The feature point associated with each of the triangular polygons may be the feature point that is closest to the triangular polygon and/or corresponds to the triangular polygon in position.
[0013] In a step 250, each of the triangular polygons associated with a feature point is moved toward the associated feature point such that vertices of each of the triangular polygons are moved toward the boundary of the structure of interest, deforming the deformable model to adapt to the structure of the interest in the volumetric image. The deformable model is deformed until a position of each of the triangular polygons corresponds to a position of the associated feature point and/or the vertices of the triangular polygon lie substantially along the boundary of the structure of interest, as shown in Fig. 4. Once the deformable model has deformed such that the triangular polygons correspond to the associated feature points of the boundary of the structure of interest, the deformable model has been adapted to the structure of interest such that the deformed deformable model represents a segmented structure of the structure of interest.
[0014] Upon completion of the segmentation process, the user may enter a user input, in a step 260, regarding the segmented brain structure. The user input may be entered via the graphical user interface 104, electing a user option, which may be displayed on the graphical user interface 104. For example, the user may elect to enlarge and/or zoom into a particular portion of the displayed images, change a view of a particular image, determine parameters of interest (e.g., segmented structure volume, curvature at a point), identify a deformation in the segmented structure, etc. Other options may include storing the segmented structure and/or the corresponding volumetric images in the database of deformable models or recalling previously stored segmented structures from the database for comparison purposes. It will be understood by those of skill in the art that the segmented structures and/or corresponding volumetric images may also be stored within patient files to facilitate analysis of structural atrophy in TBI patients.
[0015] The user may desire to determine the volume and/or curvature of the segmented structure to assess changes in the brain region. Such parameters may be especially useful in linking a patient's past exposure to TBI to current sustained complaints, deficits and disability. In addition, healthy brain structures are known to be symmetrical with respect to the mid- sagittal plane such that the left and right hemispheres of the brain are mirror images of one another. Thus, in a healthy brain, a vertex in one hemisphere of the brain - in the left hemisphere, for example - should be mirrored in the other hemisphere - the right hemisphere. TBI, however, is a mostly asymmetrical disease. Thus, deviations from mean vertex values represent variances that indicate a severity of deformation of brain structures of interest. The user may therefore elect to view the deviations from mean vertex values of the segmented structure. In a further embodiment, different deviations may be color-coded for easy visualization and interpretation of results .
[0016] In a step 270, the processor 102 generates a response to the user input entered in the step 260. For example, if the user has requested the segmented structure volume, the processor 102 will calculate the volume and display the volume on the display 106. If the user has indicated the user would like to enlarge a particular portion of the volumetric image and/or segmented organ, the processor 102 will generate and display an enlarged view of the particular portion desired. In another example, if the user has indicated that the user would like to identify deformations in the segmented structure, the processor 102 will identify a mid-sagittal plane, identify deviations in mean vertex values between the left and right hemispheres and display the deformations on the display 106. As described above, the different deviations may be color-coded. The steps 260 - 270 may be repeated as desired, until the user has elected all desired options in regard to the segmented structure of the brain .
[0017] It will be apparent to those skilled in the art that various modifications and variations can be made in the structure and methodology described herein. Thus, it is intended that the present disclosure cover any modifications and variations provided that they come within the scope of the appended claims and their equivalents.
[0018] It is also noted that the claims may include reference signs /numerals in accordance with PCT Rule 6.2(b) . However, the present claims should not be considered to be limited to the exemplary embodiments corresponding to the reference signs /numerals .

中文:
[0001]创伤性脑损伤(TBI)是长期残疾的最常见原因之一。几种皮层下结构例如胼胝体、海马体、小脑、丘脑和尾核的异常与TBI是有关联的。因而,在3D中识别具有TBI的个体的神经病理是很重要的。然而,方法学上的挑战已经妨碍了先前研究提供在TBI之后的结构性萎缩的清晰图形。
[0005]图1示出了根据示例性实施例的系统的示意图。
[0006]图2示出了根据示例性实施例的方法的流程图。
[0007]图3示出了在于GUI上显示的体积图像中初始化的可变形脑部模型的屏幕截图。
[0008]图4示出了在针对体积图像已经进行调整之后的图3的可变形脑部模型的屏幕截图。
[0009]可进一步参照说明书和附图来理解示例性实施例,其中,用相同的附图标记指示相似的元件。示例性实施例涉及用于分割脑部结构的系统和方法。具体而言,示例性实施例生成脑部结构的可变形模型,该可变形模型可以针对诸如MRI的体积图像进行调整。然而,本领域技术人员将理解的是,虽然示例性实施例特定描述了分割脑部结构,但是本发明的系统和方法可用于分割诸如MRI和/或超声图像的体积图像中的任何3维解剖结构。
[0010]如图1所示,根据示例性实施例的系统100分割诸如MRI或者超声图像的体积图像的3D脑部结构,例如胼胝体、海马体、小脑、丘脑和尾核。系统100包括处理器102,其能够基于图像中结构的特征来调整脑部结构的可变形模型。该可变形模型选自存储在存储器108中的模型数据库。利用图形用户界面104来输入用户偏好以确定脑部结构的体积,显示脑部结构的变形,观察脑部结构的特定部分,等等。经由诸如鼠标、触控显示器和/或键盘来录入与图形用户界面相关联的输入。在显示器106上显示脑部结构的分割、体积图像和图形用户界面104的用户选项。存储器108可是任何已知类型的计算机可读存储介质。本领域技术人员将理解的是,系统100例如是个人计算机、服务器或者任何其他的处理装置。
[0011]图2示出了根据示例性实施例的方法200,其中系统100分割脑部结构以识别该脑部结构中的变形。方法200包括在步骤210中,从存储在存储器108中的结构模型数据库中选取感兴趣脑部结构的可变形模型。在示例性实施例中,通过处理器102将体积图像中感兴趣脑部结构的特征与数据库中的结构模型相比较来自动选取可变形模型。在另一示例性实施例中,通过用户遍览数据库以识别出与感兴趣脑部结构最接近类似的可变形模型来手动选取可变形模型。结构模型数据库可包括来自脑部结构研究的结构模型和/或来自先前患者的分割结果。
[0012]在步骤220中,如图3所示,在显示器106上显示可变形模型。该可变形模型被显示为新的图像和/或被显示在体积图像之上。该可变形模型由表面网格形成,该表面网格包括多个三角形状的多边形,每个三角形状的多边形还包括三个顶点和边。然而,本领域技术人员将理解的是表面网格可包括其他形状的多边形。可变形模型被如此定位使得该可变形模型的顶点尽可能接近感兴趣结构的边界而定位。在步骤230中,给每个三角多边形指定最佳边界检测函数。在步骤240中,最佳边界检测函数沿着感兴趣结构的边界检测特征点从而使得每个三角多边形与特征点相关。特征点可与每个三角多边形的中心相关联。与每个三角多边形相关联的特征点可是与该三角多边形最接近的特征点和/或与就位的三角多边形对应的特征点。
[0013]在步骤250中,将与特征点相关联的每个三角多边形朝着相关联的特征点移动从而使得朝着感兴趣结构的边界移动每个三角多边形的顶点,将该可变形模型变形以适应体积图像中感兴趣的结构。如图4所示,变形该可变形模型直到每个三角多边形的位置对应于相关联的特征点的位置和/或该三角多边形的顶点基本位于沿着感兴趣结构的边界。一旦可变形模型已经变形从而使得三角多边形对应于感兴趣结构的边界的相关联特征点,那么该可变形模型已经被适应于感兴趣结构从而使得经变形的可变形模型代表感兴趣结构的分割结构。
[0014]当完成分割过程时,在步骤260中,用户可关于被分割的脑部结构而录入用户输入。可经由图形用户界面104,选取可显示在图形用户界面104上的用户选项来录入用户输入。例如,用户可选择扩大和/或放大所显示图像的特定部分、改变特定图像的视图、确定感兴趣的参数(例如,被分割结构体积、一点处的曲率)、识别在被分割结构中的变形,等等。其他选项可包括存储被分割结构和/或可变形模型数据库中对应的体积图像或者出于比较目的从数据库取回先前存储的被分割结构。本领域技术人员将理解的是,被分割结构和/或对应的体积图像也可被存储在患者档案中以便于对TBI患者中结构性萎缩的分析。
[0015]用户可期望确定被分割结构的体积和/或曲率以评估脑部区域的变化。这种参数可尤其用于将患者过去对TBI的暴露联系于当前承受的病症、缺陷和残疾。此外,已知健康脑部结构相对于正中矢状面是对称的,从而使得脑部的左和右半球是彼此的镜面图像。因而,在健康的脑部中,在脑部一个半球中-例如左半球中-的顶点,应当被反映在另一半球-右半球中。然而,TBI是最不对称的疾病。因而,与平均顶点值的偏差代表指示感兴趣脑部结构的变形严重度的方差。因此,用户可选择观察与被分割结构的平均顶点值的偏差。在另一实施例中,可对不同的偏差进行颜色编码以容易进行结果的可视化和解释。
[0016]在步骤270中,处理器102生成对在步骤260中录入的用户输入的响应。例如,如果用户已经要求了被分割结构体积,那么处理器102将计算体积并且在显示器106上显示该体积。如果用户已经指示该用户想要扩大体积图像和/或被分割器官的特定部分,那么处理器102将生成并显示期望的特定部分的扩大视图。在另一实施例中,如果用户已经指示该用户想要识别在被分割结构中的变形,那么处理器102将识别正中矢状面,识别在左和右半球之间的平均顶点值的偏差,并在显示器106上显示变形。如上所述,可对不同的偏差进行颜色编码。可按需求重复步骤260-270,直到用户已经选取了关于脑部被分割结构的所有期望选项。
[0017]对本领域技术人员而言显而易见的是,可以对在本文中描述的结构和方法做出各种修改和变型。因而,本发明的公开旨在覆盖任意修改和变型,只要它们落在权利要求书和其等价物的范围之内。
[0018]也注意到权利要求可包括根据PCT细则6.2(b)的附图符号/标记。然而,权利要求不应解释为限于与附图符号/标记对应的示例性实施例。