Deformable M-reps for 3D medical image segmentation
Deformable M-reps for 3D medical image segmentation
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DOI：
10.1023/a:1026313132218
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发表时间：
2003-11-01
影响因子：
19.5
通讯作者：
Chaney, EL
中科院分区：
文献类型：
--
作者：
Pizer, SM;Fletcher, PT;Chaney, EL
M-reps（以前称为DSL）是一种用于建模和渲染3D实体几何的多尺度中间方法。它们特别适合于对解剖对象进行建模，特别是在可变形模型分割方法中有效地捕获先前的几何信息。该表示基于图形模型，其通过图形的层次结构以粗略的比例定义对象-每个图形通常是同时表示固体区域及其边界的板。本文主要研究利用单个图形模型来分割结构相对简单的物体，单个图形是一张由网格形成的模型内插而成的中间原子，中间原子的网格或链（因此称为m-reps），每个原子不仅通过位置和宽度，而且通过局部图形框架来建模实体区域，该局部图形框架给出图形方向和由m-rep暗示的边界上的相对的对应位置之间的对象角度。m-rep的特殊能力是提供处于两种不同变形状态的对象之间的空间和定向对应。这种能力是中央的有效测量的几何典型性和几何图像匹配，两个条款的目标函数的分割可变形模型优化。m-reps的另一个能力是有效分割的核心是它们能够支持多个尺度级别的分割，并具有连续更高的精度。由单个图形建模的对象首先通过由对象伸长增强的相似性变换进行分割，然后通过调整每个中间原子，最后通过置换m-rep隐含边界的密集采样进行分割。虽然这些模型和方法也存在于2D中，但我们专注于3D对象，本文以CT中的肾脏和MRI中的海马分割为主要例子。报告了与手动逐切片分割相比的分割准确性。
M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures - each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure.A single figure is a sheet of medial atoms, which is interpolated from the model formed by a net, i.e., a mesh or chain, of medial atoms (hence the name m-reps), each atom modeling a solid region via not only a position and a width but also a local figural frame giving figural directions and an object angle between opposing, corresponding positions on the boundary implied by the m-rep. The special capability of an m-rep is to provide spatial and orientational correspondence between an object in two different states of deformation. This ability is central to effective measurement of both geometric typicality and geometry to image match, the two terms of the objective function optimized in segmentation by deformable models. The other ability of m-reps central to effective segmentation is their ability to support segmentation at multiple levels of scale, with successively finer precision. Objects modeled by single figures are segmented first by a similarity transform augmented by object elongation, then by adjustment of each medial atom, and finally by displacing a dense sampling of the m-rep implied boundary. While these models and approaches also exist in 2D, we focus on 3D objects.The segmentation of the kidney from CT and the hippocampus from MRI serve as the major examples in this paper. The accuracy of segmentation as compared to manual, slice-by-slice segmentation is reported.