Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences
Network Flow Integer Programming to Track Elliptical Cells in Time-Lapse Sequences
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DOI：
10.1109/tmi.2016.2640859
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发表时间：
2017
影响因子：
10.6
通讯作者：
Engin Türetken;Xinchao Wang;C. Becker;Carsten Haubold;P. Fua
中科院分区：
文献类型：
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作者：
Engin Türetken;Xinchao Wang;C. Becker;Carsten Haubold;P. Fua
我们提出了一种自动跟踪延时图像序列中椭圆细胞群的新方法。给定初始分割，我们通过生成一组竞争检测假设来解释部分遮挡和重叠。为此，我们将椭圆拟合到初始区域的某些部分，并构建椭圆层次结构，然后将其视为候选单元格。然后，我们通过求解只有一类流变量的整数程序的最优性来选择时间一致的流变量。这消除了启发式处理由于部分闭塞和复杂形态而错过的检测的需要。我们证明了我们的方法在一系列具有挑战性的序列上的有效性，这些序列由团块细胞组成，并表明它优于最先进的技术。
We propose a novel approach to automatically tracking elliptical cell populations in time-lapse image sequences. Given an initial segmentation, we account for partial occlusions and overlaps by generating an over-complete set of competing detection hypotheses. To this end, we fit ellipses to portions of the initial regions and build a hierarchy of ellipses, which are then treated as cell candidates. We then select temporally consistent ones by solving to optimality an integer program with only one type of flow variables. This eliminates the need for heuristics to handle missed detections due to partial occlusions and complex morphology. We demonstrate the effectiveness of our approach on a range of challenging sequences consisting of clumped cells and show that it outperforms state-of-the-art techniques.