Robust Tracking of Migrating Cells Using Four-Color Level Set Segmentation
Robust Tracking of Migrating Cells Using Four-Color Level Set Segmentation
复制标题
使用四色水平集分割对迁移细胞进行鲁棒跟踪
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
K. Palaniappan
中科院分区：
文献类型：
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作者：
S. Nath;F. Bunyak;K. Palaniappan
了解迁移细胞的行为正在成为一个新兴的研究领域，具有许多重要的应用。分割和跟踪是本研究的重要步骤。在本文中，我们提出了一个自动细胞分割和跟踪系统，旨在研究细胞的迁移与相衬显微镜成像。在分割方面，系统采用了主动轮廓水平集方法，并进行了新颖的扩展，有效地防止了假合并问题。跟踪是通过使用多距离、多假设算法解决多个细胞之间的帧间对应来完成的。进入视野的细胞，因细胞分裂而产生的细胞或因细胞凋亡而消失的细胞被系统可靠地分割和跟踪。由于难以分割密集的细胞簇，用相差显微镜成像的细胞鲁棒跟踪是一个具有挑战性的问题。由于被成像的细胞边界模糊，邻近的细胞可能出现合并。这些错误的合并导致在跟踪过程中生成不正确的轨迹。当前解决假合并问题的基于水平集的方法需要每个对象有一个唯一的水平集（n -水平集范式）。所提出的方法使用以前的帧和图形着色原则的证据，并解决了同样的问题，对于任意数量的类似对象，如细胞，只有四个水平集。
Understanding behavior of migrating cells is becoming an emerging research area with many important applications. Segmentation and tracking constitute vital steps of this research. In this paper, we present an automated cell segmentation and tracking system designed to study migration of cells imaged with a phase contrast microscope. For segmentation the system uses active contour level set methods with a novel extension that efficiently prevents false-merge problem. Tracking is done by resolving frame to frame correspondences between multiple cells using a multi-distance, multi-hypothesis algorithm. Cells that move into the field-of-view, arise from cell division or disappear due to apoptosis are reliably segmented and tracked by the system. Robust tracking of cells, imaged with a phase contrast microscope is a challenging problem due to difficulties in segmenting dense clusters of cells. As cells being imaged have vague borders, close neighboring cells may appear to merge. These false-merges lead to incorrect trajectories being generated during the tracking process. Current level-set based approaches to solve the false-merge problem require a unique level set per object (the N-level set paradigm). The proposed approach uses evidence from previous frames and graph coloring principles and solves the same problem with only four level sets for any arbitrary number of similar objects, like cells.