The deformable most-likely-point paradigm.
The deformable most-likely-point paradigm.
复制标题
可变形最有可能点范式。
DOI：
10.1016/j.media.2019.04.013
复制
发表时间：
2019
影响因子：
10.9
通讯作者：
Taylor,RussellH
中科院分区：
文献类型：
--
作者：
Sinha,Ayushi;Billings,SethD;Reiter,Austin;Liu,Xingtong;Ishii,Masaru;Hager,GregoryD;Taylor,RussellH
在本文中，我们提出了三种可变形配准算法，该算法在使用3D统计形状模型的范例中设计，以同时完成两项任务：1）将来自先前未见过的数据的点特征配准到统计导出的形状（例如，该范例被称为可变形最可能点范例（deformable most-likely-pointparadigm），其动机是基于可用数据构建的生成形状模型可以用于估计先前未见过的数据。我们开发了三个变形注册算法在此范例中使用统计形状模型建立可靠的分割对象与对应。几个实验的结果表明，我们的算法产生准确的注册和重建在各种应用程序的错误高达CT分辨率的医疗数据集。我们的代码可在https://github.com/AyushiSinha/cisstICP上获得。
In this paper, we present three deformable registration algorithms designed within a paradigm that uses 3D statistical shape models to accomplish two tasks simultaneously:1) register point features from previously unseen data to a statistically derived shape (e.g., mean shape), and2) deform the statistically derived shape to estimate the shape represented by the point features.This paradigm, called thedeformable most-likely-pointparadigm, is motivated by the idea that generative shape models built from available data can be used to estimate previously unseen data. We developed three deformable registration algorithms within this paradigm using statistical shape models built from reliably segmented objects with correspondences. Results from several experiments show that our algorithms produce accurate registrations and reconstructions in a variety of applications with errors up to CT resolution on medical datasets. Our code is available at https://github.com/AyushiSinha/cisstICP.