IEEE Trans Med Imaging. 2016 Jul;35(7):1625-35. doi: 10.1109/TMI.2016.2521653. Epub 2016 Jan 25.
We propose a novel method for tracking cells that are connected through a visible network of membrane junctions. Tissues of this form are common in epithelial cell sheets and resemble planar graphs where each face corresponds to a cell. We leverage this structure and develop a method to track the entire tissue as a deformable graph. This coupled model in which vertices inform the optimal placement of edges and vice versa captures global relationships between tissue components and leads to accurate and robust cell tracking. We compare the performance of our method with that of four reference tracking algorithms on four data sets that present unique tracking challenges. Our method exhibits consistently superior performance in tracking all cells accurately over all image frames, and is robust over a wide range of image intensity and cell shape profiles. This may be an important tool for characterizing tissues of this type especially in the field of developmental biology where automated cell analysis can help elucidate the mechanisms behind controlled cell-shape changes.
我们提出了一种新的方法来跟踪通过可见的膜连接网络连接的细胞。这种形式的组织在上皮细胞片层中很常见，类似于平面图形，其中每个面都对应一个细胞。我们利用这种结构并开发了一种方法来将整个组织作为可变形图进行跟踪。在这种耦合模型中，顶点通知边缘的最佳放置位置，反之亦然，从而捕获了组织成分之间的全局关系，并实现了准确和鲁棒的细胞跟踪。我们将我们的方法与四个参考跟踪算法在四个呈现独特跟踪挑战的数据集上的性能进行了比较。我们的方法在所有图像帧上准确跟踪所有细胞的性能始终优于其他方法，并且在广泛的图像强度和细胞形状轮廓范围内具有鲁棒性。这可能是这种类型的组织特征描述的重要工具，特别是在发育生物学领域，自动细胞分析可以帮助阐明控制细胞形状变化背后的机制。
