A Diffeomorphic Mapping Based Characterization of Temporal Sequences: Application to the Pelvic Organ Dynamics Assessment
A Diffeomorphic Mapping Based Characterization of Temporal Sequences: Application to the Pelvic Organ Dynamics Assessment
复制标题
基于微分形映射的时间序列表征：在盆腔器官动力学评估中的应用
DOI：
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发表时间：
2013
影响因子：
2
通讯作者：
N. Pirró
中科院分区：
文献类型：
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作者：
M. Rahim;Marc;R. Bulot;N. Pirró
在各种成像应用中，研究形状变化是为了定义所涉及的转换或量化所执行的每次变化之间的距离。无论以何种方式提取形状，在2D成像中，形状基本上与曲线或点集有关，这取决于可用的数据。无论时间是否与形状变化有关，我们都可以将一组形状视为对初始形状的时间演变的观察。在这种情况下，我们提出了一种方法，旨在量化一组没有地标的轮廓的演变。我们对时间序列的表征是基于大变形微分同构映射范式和基于电流的形状表示，这允许两者提出形状度量和时间变化的曲线匹配。然后，提取与机制相关的特征，因为它们具有物理意义，并且非常容易理解。在本文中，该过程应用于盆腔阴部学研究的范围内。现有的临床诊断与统计分析相结合，以显示该方法的合理性。事实上，盆底疾病的特点是异常器官下降和变形在腹部劳损。由于盆腔器官是软组织器官，除了形状差异很大外，没有固定的标志。常规使用的二维矢状面mri序列被分割，以提供轮廓集，从中表征应该突出盆腔器官的行为。我们认为，对这些动态mri序列的行为进行统计分析可以帮助更好地了解盆底病理生理。该方法应用于具有不同临床诊断的30例患者的数据集。通过患者间分析，提出了一些有希望的结果，其中评估了变形特征的病理检测能力，并计算了主要器官动力学模式。此外，由于局部变形分析，提出了器官分割，它确定了与临床相关的空间参考。
In various imaging applications, shape variations are studied in order to define the transformations involved or to quantify a distance between each change performed. Regardless of the way the shapes may be extracted, with 2D imaging, shapes concern essentially curves or sets of points depending on the available data. Wether time is related to the shape variations or not, one can consider a set of shapes as the observation of the temporal evolution of an initial shape. In this context, we present a methodology aiming at quantifying the evolution of a set of contours without landmarks. Our characterization of temporal sequences is based on the large deformation diffeomorphic mapping paradigm and the shape representation based on currents, which allow both to propose a shape metric and a curve matching of the timed variations. Then, mechanics related features are extracted as they are physically meaningful and quite painless understandable.In this paper, the process is applied within the scope of a pelviperineology study. Available clinical diagnoses are combined with statistical analysis to show the soundness of the approach. Indeed, pelvic floor disorders are characterized by abnormal organ descents and deformations during abdominal strains. As they are soft-tissue organs, the pelvic organs have no fixed landmarks, in addition to wide shape differences. Routinely used, 2D sagittal mri sequences are segmented to provide the contour sets from which the characterization should highlight pelvic organ behaviors. We believe that a statistical analysis of these behaviors on several dynamic mri sequences could help to a better understanding of the pelvic floor pathophysiology. The methodology is applied on a dataset of 30 patients with different clinical diagnoses. Some promising results are presented, where the pathology detection capability of the deformation features is assessed, and the principal organ dynamics modes are computed, through an inter-patient analysis. Also, an organ parcellation is proposed thanks to the local deformation analysis, it identifies spatial references which are clinically relevant.