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本文引用的文献

1
A Multi-Compartment Segmentation Framework With Homeomorphic Level Sets.一种具有同胚水平集的多隔室分割框架。
Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit. 2008:1-6. doi: 10.1109/CVPR.2008.4587475.
2
Snakes, shapes, and gradient vector flow.蛇形、形状与梯度向量流。
IEEE Trans Image Process. 1998;7(3):359-69. doi: 10.1109/83.661186.
3
Active contours without edges.无边缘活动轮廓。
IEEE Trans Image Process. 2001;10(2):266-77. doi: 10.1109/83.902291.
4
Statistical and topological atlas based brain image segmentation.基于统计和拓扑图谱的脑图像分割
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):94-101. doi: 10.1007/978-3-540-75757-3_12.
5
Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model.使用概率图谱和多级统计形状模型从3D CT图像中自动分割肝脏。
Med Image Comput Comput Assist Interv. 2007;10(Pt 1):86-93. doi: 10.1007/978-3-540-75757-3_11.
6
Digital homeomorphisms in deformable registration.可变形配准中的数字同胚
Inf Process Med Imaging. 2007;20:211-22. doi: 10.1007/978-3-540-73273-0_18.
7
Active mean fields: solving the mean field approximation in the level set framework.有源平均场：在水平集框架中求解平均场近似
Inf Process Med Imaging. 2007;20:26-37. doi: 10.1007/978-3-540-73273-0_3.
8
A shape-guided deformable model with evolutionary algorithm initialization for 3D soft tissue segmentation.一种用于三维软组织分割的、具有进化算法初始化的形状引导可变形模型。
Inf Process Med Imaging. 2007;20:1-12. doi: 10.1007/978-3-540-73273-0_1.
9
Global regularizing flows with topology preservation for active contours and polygons.用于活动轮廓和多边形的具有拓扑保持性的全局正则化流。
IEEE Trans Image Process. 2007 Mar;16(3):803-12. doi: 10.1109/tip.2007.891071.
10
Segmentation of thalamic nuclei from DTI using spectral clustering.使用谱聚类从扩散张量成像（DTI）中分割丘脑核团。
Med Image Comput Comput Assist Interv. 2006;9(Pt 2):807-14. doi: 10.1007/11866763_99.

一种用于同胚三维医学图像分割的多重几何可变形模型框架。

A Multiple Geometric Deformable Model Framework for Homeomorphic 3D Medical Image Segmentation.

作者信息

Fan Xian, Bazin Pierre-Louis, Bogovic John, Bai Ying, Prince Jerry L

出版信息

Conf Comput Vis Pattern Recognit Workshops. 2008 Jul 15;2008:1-7. doi: 10.1109/CVPRW.2008.4563013.

DOI:10.1109/CVPRW.2008.4563013
PMID:22140657
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3227018/
Abstract

This paper presents a 3D segmentation framework for multiple objects or compartments embedded as level sets. Thanks to a compact representation of the level set functions of multiple objects, the framework guarantees no overlap and vacuum, and leads to a computationally efficient evolution scheme largely independent of the number of objects. Appropriate topology constraints ensure not only that the topology of each object remains the same, but that the relationship between objects is also maintained. The decomposition of objects makes the framework specifically attractive to the segmentation of related anatomical regions or the parcellation of an organ, where relationships must be maintained and different evolution forces are needed on different parts of the objects interface. Examples of 3D whole brain segmentation and thalamic parcellation demonstrate the potential of our method for such segmentation tasks.

摘要

本文提出了一种用于嵌入水平集的多个对象或区域的三维分割框架。由于多个对象的水平集函数的紧凑表示，该框架保证无重叠和空洞，并导致一种计算效率高的演化方案，很大程度上独立于对象数量。适当的拓扑约束不仅确保每个对象的拓扑保持不变，而且确保对象之间的关系也得以维持。对象的分解使得该框架对于相关解剖区域的分割或器官的划分特别有吸引力，在这些任务中必须维持关系，并且在对象界面的不同部分需要不同的演化力。三维全脑分割和丘脑划分的示例证明了我们的方法在此类分割任务中的潜力。