Hong Sungmin, Fishbaugh James, Gerig Guido
Computer Science and Engineering, Tandon School of Engineering, New York University.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1014-1017. doi: 10.1109/ISBI.2018.8363743. Epub 2018 May 24.
Longitudinal shape analysis has shown great potential to model anatomical processes from baseline to follow-up observations. Shape regression estimates a continuous trajectory of time-discrete anatomical shapes to quantify temporal changes. The need for shape alignment and point-to-point correspondences represent limitations of current shape analysis methodologies, and present significant challenges in shape evaluation. We propose a method that estimates a continuous trajectory of continuous medial representations (CM-Rep) from a set of time-discrete observed shapes. To avoid the traditional step of aligning individual objects, shape changes are modeled via diffeomorphic ambient space deformations. Using a medial shape representation, we separately capture object pose changes and intrinsic geometry changes. Tests and validation with synthetic and real anatomical shapes demonstrate that the new method captures extrinsic shape changes as well as intrinsic shape changes encoded with CM-Reps, a highly relevant property for studying growth and disease processes.
纵向形状分析在对从基线到随访观察的解剖过程进行建模方面显示出巨大潜力。形状回归估计时间离散的解剖形状的连续轨迹,以量化时间变化。形状对齐和点对点对应关系的需求代表了当前形状分析方法的局限性,并在形状评估中带来了重大挑战。我们提出了一种方法,该方法从一组时间离散的观察形状中估计连续中间表示(CM-Rep)的连续轨迹。为了避免对齐单个对象的传统步骤,通过微分同胚环境空间变形对形状变化进行建模。使用中间形状表示,我们分别捕获对象姿态变化和内在几何变化。对合成和真实解剖形状的测试与验证表明,新方法能够捕获外在形状变化以及由CM-Rep编码的内在形状变化,这是研究生长和疾病过程的一个高度相关的特性。