IEEE Trans Med Imaging. 2017 Apr;36(4):942-951. doi: 10.1109/TMI.2016.2640859. Epub 2016 Dec 15.
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.
我们提出了一种新的方法，用于自动跟踪时变图像序列中的椭圆形细胞群体。给定初始分割，我们通过生成一组完整的竞争检测假设来考虑部分遮挡和重叠。为此，我们将椭圆拟合到初始区域的一部分，并构建椭圆层次结构，然后将其视为细胞候选者。然后，我们通过求解具有仅一种流变量类型的整数规划来最优地选择时间一致的变量，从而消除了由于部分遮挡和复杂形态而需要使用启发式方法来处理漏检的情况。我们在一系列由聚集细胞组成的具有挑战性的序列上展示了我们方法的有效性，并表明它优于最先进的技术。
