[论文解读] Benchmarking off-the-shelf statistical shape modeling tools in clinical applications

Anupama Goparaju, Alexandre Bône+9 more|arXiv (Cornell University)|Sep 6, 2020

Morphological variations and asymmetry参考文献 87被引用 47

一句话总结

本文在临床相关性方面，通过外在验证框架，对三个开源统计形状建模工具（ShapeWorks、Deformetrica、SPHARM-PDM）进行基准评估。

ABSTRACT

Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes. Technological advancements of in vivo imaging have led to the development of open-source computational tools that automate the modeling of anatomical shapes and their population-level variability. However, little work has been done on the evaluation and validation of such tools in clinical applications that rely on morphometric quantifications (e.g., implant design and lesion screening). Here, we systematically assess the outcome of widely used, state-of-the-art SSM tools, namely ShapeWorks, Deformetrica, and SPHARM-PDM. We use both quantitative and qualitative metrics to evaluate shape models from different tools. We propose validation frameworks for anatomical landmark/measurement inference and lesion screening. We also present a lesion screening method to objectively characterize subtle abnormal shape changes with respect to learned population-level statistics of controls. Results demonstrate that SSM tools display different levels of consistencies, where ShapeWorks and Deformetrica models are more consistent compared to models from SPHARM-PDM due to the groupwise approach of estimating surface correspondences. Furthermore, ShapeWorks and Deformetrica shape models are found to capture clinically relevant population-level variability compared to SPHARM-PDM models.

研究动机与目标

• 在临床场景中，如植入物设计、计划和病变筛查，推动对 SSM 工具的评估与验证。
• 比较 ShapeWorks、Deformetrica 和 SPHARM-PDM 在一致性和群体水平变异性捕获方面的能力。
• 提出解剖标志物推断和病变筛查的验证框架。
• 展示群体水平统计如何为客观临床决策提供信息。

提出的方法

• 回顾并总结 ShapeWorks、Deformetrica 和 SPHARM-PDM 的建模方法。
• 开发不依赖真实对应关系的外在评估与验证框架。
• 对每个工具使用数据预处理、基于重要性采样的数据分割和超参数调整。
• 利用紧凑度、泛化能力和特异性等指标评估形状模型。
• 通过变化模态和聚类分析进行定性评估，以识别临床相关模式。

实验结果

研究问题

• RQ1 开源 SSM 工具在为整个群体生成一致对应关系方面有何差异？
• RQ2 哪些工具更能捕捉到临床相关的群体水平变异？
• RQ3 在缺乏真实对应关系时，外在验证框架是否能评估形状模型的临床有用性？
• RQ4 基于 SSM 的解剖标志物推断和病变筛查是否能为临床决策提供客观指引？
• RQ5 工具特定假设（groupwise 与 pairwise）对形状建模结果有何影响？

主要发现

• 由于群组内对应关系估计，ShapeWorks 和 Deformetrica 的一致性高于 SPHARM-PDM。
• ShapeWorks 和 Deformetrica 往往比 SPHARM-PDM 更有效地捕捉群体水平变异性。
• 所提出的验证框架，即使在没有真实对应关系的情况下，也能评估解剖标志物推断和病变筛查。
• 外在评估揭示了各工具在紧凑性、泛化性和特异性方面的差异。
• 对变化模态和聚类的定性分析揭示了形状群体中的可临床解释的模式。