Statistical multi-level shape models for scalable modeling of multi-organ anatomies.
Statistical multi-level shape models for scalable modeling of multi-organ anatomies.
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DOI：
10.3389/fbioe.2023.1089113
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发表时间：
2023
影响因子：
5.7
通讯作者：
中科院分区：
文献类型：
--
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统计形状建模是解剖学定量分析中不可或缺的工具。基于粒子的形状建模 (PSM) 是一种最先进的方法，可以从医学成像数据（例如 CT、MRI）以及由此生成的相关 3D 解剖模型中学习群体级别的形状表示。 PSM 优化了给定形状群组上一组密集标志（即对应点）的放置。 PSM 通过全局统计模型支持多器官建模作为传统单器官框架的特殊情况，其中多结构解剖学被视为单个结构。然而，全局多器官模型对于许多器官来说是不可扩展的，导致解剖学不一致，并导致纠缠的形状统计数据，其中形状变化模式反映了器官内和器官间的变化。因此，需要一种有效的建模方法，可以捕获复杂解剖结构的器官间关系（即姿势变化），同时优化每个器官的形态变化并捕获群体水平的统计数据。本文利用 PSM 方法，提出了一种克服这些限制的多器官对应点优化的新方法。多级成分分析的中心思想是形状统计由两个相互正交的子空间组成：器官内子空间和器官间子空间。我们使用该生成模型制定对应优化目标。我们使用脊柱、脚和踝关节以及髋关节的关节结构的合成形状数据和临床数据来评估所提出的方法。
Statistical shape modeling is an indispensable tool in the quantitative analysis of anatomies. Particle-based shape modeling (PSM) is a state-of-the-art approach that enables the learning of population-level shape representation from medical imaging data (e.g., CT, MRI) and the associated 3D models of anatomy generated from them. PSM optimizes the placement of a dense set of landmarks (i.e., correspondence points) on a given shape cohort. PSM supports multi-organ modeling as a particular case of the conventional single-organ framework via a global statistical model, where multi-structure anatomy is considered as a single structure. However, global multi-organ models are not scalable for many organs, induce anatomical inconsistencies, and result in entangled shape statistics where modes of shape variation reflect both within- and between-organ variations. Hence, there is a need for an efficient modeling approach that can capture the inter-organ relations (i.e., pose variations) of the complex anatomy while simultaneously optimizing the morphological changes of each organ and capturing the population-level statistics. This paper leverages the PSM approach and proposes a new approach for correspondence-point optimization of multiple organs that overcomes these limitations. The central idea of multilevel component analysis, is that the shape statistics consists of two mutually orthogonal subspaces: the within-organ subspace and the between-organ subspace. We formulate the correspondence optimization objective using this generative model. We evaluate the proposed method using synthetic shape data and clinical data for articulated joint structures of the spine, foot and ankle, and hip joint.
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影响因子：
2.8
作者：
Jones, Kevin B.;Datar, Manasi;Ravichandran, Sandhya;Jin, Huifeng;Jurrus, Elizabeth;Whitaker, Ross;Capecchi, Mario R.
通讯作者：
Capecchi, Mario R.
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通讯作者：
Simmons, Andrew
影响因子：
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作者：
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通讯作者：
Gielis, J
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8.6
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Carriere, Nicolas;Besson, Pierre;Devos, David
通讯作者：
Devos, David