Volume 26, Issue 4

In this paper, we focus on the retrospective topology correction of surfaces. We propose a technique to accurately correct the spherical topology of cortical surfaces. Specifically, we construct a mapping from the original surface onto the sphere to detect topological defects as minimal nonhomeomorphic regions. The topology of each defect is then corrected by opening and sealing the surface along a set of nonseparating loops that are selected in a Bayesian framework. The proposed method is a wholly self-contained topology correction algorithm, which determines geometrically accurate, topologically correct solutions based on the magnetic resonance imaging (MRI) intensity profile and the expected local curvature. Applied to real data, our method provides topological corrections similar to those made by a trained operator

本文聚焦于表面的回顾性拓扑校正。我们提出了一种技术，用于精确校正皮层表面的球面拓扑。具体而言，我们构建从原始表面到球面的映射，以将拓扑缺陷检测为最小的非同胚区域。然后通过沿着在贝叶斯框架中选择的一组非分离环打开和密封表面来校正每个缺陷的拓扑。所提出的方法是一个完全自包含的拓扑校正算法，它基于磁共振成像（MRI）强度轮廓和预期的局部曲率来确定几何精确、拓扑正确的解。应用于实际数据时，我们的方法提供了与经过训练的操作员所做的相似的拓扑校正。

Functional localization is a concept which involves the application of a sequence of geometrical and statistical image processing operations in order to define the location of brain activity or to produce functional/parametric maps with respect to the brain structure or anatomy. Considering that functional brain images do not normally convey detailed structural information and, thus, do not presen...

功能定位是一个概念，它涉及应用一系列几何和统计图像处理操作来定义大脑活动的位置，或根据大脑结构或解剖学生成功能/参数图。考虑到功能脑图像通常不传达详细的结构信息，因此不呈现...

Atlas-based approaches have demonstrated the ability to automatically identify detailed brain structures from 3-D magnetic resonance (MR) brain images. Unfortunately, the accuracy of this type of method often degrades when processing data acquired on a different scanner platform or pulse sequence than the data used for the atlas training. In this paper, we improve the performance of an atlas-based...

基于图谱的方法已证明能够从三维磁共振（MR）脑图像中自动识别详细的脑结构。不幸的是，当处理在与图谱训练所用数据不同的扫描仪平台或脉冲序列上获取的数据时，这类方法的准确性往往会下降。在本文中，我们改进了基于图谱的性能...

We present a novel weighted Fourier series (WFS) representation for cortical surfaces. The WFS representation is a data smoothing technique that provides the explicit smooth functional estimation of unknown cortical boundary as a linear combination of basis functions. The basic properties of the representation are investigated in connection with a self-adjoint partial differential equation and the...

我们提出了一种新的皮质表面加权傅里叶级数（WFS）表示。WFS表示是一种数据平滑技术，它通过基函数的线性组合提供了未知皮质边界的显式平滑函数估计。该表示的基本性质与自伴偏微分方程有关，并进行了研究...

In large-deformation diffeomorphic metric mapping (LDDMM), the diffeomorphic matching of images are modeled as evolution in time, or a flow, of an associated smooth velocity vector field v controlling the evolution. The initial momentum parameterizes the whole geodesic and encodes the shape and form of the target image. Thus, methods such as principal component analysis (PCA) of the initial moment...

在大形变微分同胚度量映射（LDDMM）中，图像的微分同胚匹配被建模为随时间演化或流动，由相关的平滑速度矢量场v控制演化。初始动量参数化整个测地线并编码目标图像的形状和形式。因此，诸如初始动量的主成分分析（PCA）等方法...

This paper presents a new framework for multiple object segmentation in medical images that respects the topological properties and relationships of structures as given by a template. The technique, known as topology-preserving, anatomy-driven segmentation (TOADS), combines advantages of statistical tissue classification, topology-preserving fast marching methods, and image registration to enforce...

本文提出了一种新的框架，用于医学图像中的多对象分割，该框架尊重模板给出的结构的拓扑性质和关系。该技术被称为拓扑保持、解剖驱动的分割（TOADS），它结合了统计组织分类、拓扑保持快速行进方法和图像配准的优势，以强制执行...

In vivo quantification of neuroanatomical shape variations is possible due to recent advances in medical imaging and has proven useful in the study of neuropathology and neurodevelopment. In this paper, we apply a spherical wavelet transformation to extract shape features of cortical surfaces reconstructed from magnetic resonance images (MRIs) of a set of subjects. The spherical wavelet transforma...

由于医学影像的最新进展，活体神经解剖形状变化的量化成为可能，并且在神经病理学和神经发育研究中已被证明是有用的。在本文中，我们应用球面小波变换来提取从一组受试者的磁共振图像重建的皮层表面的形状特征。球面小波变换...

This paper presents a novel multiscale shape representation and segmentation algorithm based on the spherical wavelet transform. This work is motivated by the need to compactly and accurately encode variations at multiple scales in the shape representation in order to drive the segmentation and shape analysis of deep brain structures, such as the caudate nucleus or the hippocampus. Our proposed sh...

本文提出了一种基于球面小波变换的新型多尺度形状表示与分割算法。该工作的动机是需要紧凑而准确地编码形状表示中多个尺度的变化，以推动深部脑结构（如尾状核或海马体）的分割和形状分析。我们提出的...

It is important to detect and extract the major cortical sulci from brain images, but manually annotating these sulci is a time-consuming task and requires the labeler to follow complex protocols , . This paper proposes a learning-based algorithm for automated extraction of the major cortical sulci from magnetic resonance imaging (MRI) volumes and cortical surfaces. Unlike alternative methods for ...

从脑部图像中检测和提取主要脑沟非常重要，但手动标注这些脑沟是一项耗时的任务，并且要求标注者遵循复杂的协议。本文提出了一种基于学习的算法，用于从磁共振成像（MRI）体积和皮层表面自动提取主要的脑沟。与替代方法不同，该算法...

Sulcal fundi are 3-D curves that lie in the depths of the cerebral cortex and, in addition to their intrinsic value in brain research, are often used as landmarks for downstream computations in brain imaging. In this paper, we present a geometric algorithm that automatically extracts the sulcal fundi from magnetic resonance images and represents them as spline curves lying on the extracted triangu...

脑沟底是位于大脑皮质深处的三维曲线，除了在大脑研究中的内在价值外，通常还用作脑成像下游计算的标志。在本文中，我们提出了一种几何算法，可以从磁共振图像中自动提取脑沟底，并将其表示为位于提取的三角化表面上的样条曲线。

We describe here a classification system based on automatically identified cortical sulci. Multivariate recognition methods are required for the detection of complex brain patterns with a spatial distribution. However, such methods may face the well-known issue of the curse of dimensionality-the risk of overfitting the training dataset in high-dimensional space. We overcame this problem, using a c...

我们在这里描述了一个基于自动识别皮质沟的分类系统。检测具有空间分布的复杂脑模式需要多变量识别方法。然而，这类方法可能面临维度灾难的问题——在高维空间中过拟合训练数据集的风险。我们通过使用一种c...

In this paper, we present a statistical parts-based model (PBM) of appearance, applied to the problem of modeling intersubject anatomical variability in magnetic resonance (MR) brain images. In contrast to global image models such as the active appearance model (AAM), the PBM consists of a collection of localized image regions, referred to as parts, whose appearance, geometry and occurrence freque...

在本文中，我们提出了一种基于统计部件的外观模型，应用于磁共振（MR）脑图像中受试者间解剖变异性的建模问题。

We describe a knowledge-driven algorithm to automatically delineate the caudate nucleus (CN) region of the human brain from a magnetic resonance (MR) image. Since the lateral ventricles (LVs) are good landmarks for positioning the CN, the algorithm first extracts the LVs, and automatically localizes the CN from this information guided by anatomic knowledge of the structure. The face validity of th...

我们描述了一种知识驱动的算法，用于从磁共振（MR）图像中自动勾勒出人脑尾状核（CN）区域。由于侧脑室（LV）是定位CN的良好标志，该算法首先提取LV，然后根据解剖学知识自动定位CN。该算法的表面效度...

In clinical applications where structural asymmetries between homologous shapes have been correlated with pathology, the questions of definition and quantification of "asymmetry" arise naturally. When not only the degree but the position of deformity is thought relevant, asymmetry localization must also be addressed. Asymmetries between paired shapes have already been formulated in terms of (nonri...

在临床应用中，当同源形状之间的结构不对称与病理相关时，自然会产生“不对称”的定义和量化问题。当不仅考虑畸形程度，还考虑其位置时，必须解决不对称定位问题。配对形状之间的不对称已经用（非黎曼）...

Image registration methods play a crucial role in computational neuroanatomy. This paper mainly contributes to the field of image registration with the use of nonlinear spatial transformations. Particularly, problems connected to matching magnetic resonance imaging (MRI) brain image data obtained from various subjects and with various imaging conditions are solved here. Registration is driven by l...

图像配准方法在计算神经解剖学中起着至关重要的作用。本文主要贡献于使用非线性空间变换的图像配准领域。特别是，解决了匹配来自不同受试者和不同成像条件的磁共振成像（MRI）脑图像数据的问题。配准由...

Methods of computational anatomy are typically based on a spatial transformation that maps a template to an individual anatomy and vice versa. However, important morphological characteristics are frequently not captured by this transformation, thereby leading to lossy representations. We extend this formulation by incorporating residual anatomical information, i.e., information that is not capture...

计算解剖学的方法通常基于将模板映射到个体解剖结构及其逆映射的空间变换。然而，重要的形态特征常常未被这种变换捕捉，从而导致有损表示。我们通过纳入残余解剖信息（即未被捕捉的信息）来扩展这一公式。

The 16 papers in this special issue focus on the growing field of computational neuroanatomy. Some of the topics covered include: new work on deformable geometry and surface-based anatomical modeling; analysis of anatomical shape, with applications to disease classification and computer-aided diagnosis; new types of statistical analyses of structural brain images; and image registration.

本特刊中的16篇论文聚焦于计算神经解剖学这一日益发展的领域。涵盖的主题包括：可变形几何与基于表面的解剖建模的新研究；解剖形状分析及其在疾病分类和计算机辅助诊断中的应用；结构性脑图像的新型统计分析；以及图像配准。

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