Department of Engineering Science, University of Oxford, United Kingdom.
Department of Engineering Science, University of Oxford, United Kingdom.
Med Image Anal. 2014 Oct;18(7):977-88. doi: 10.1016/j.media.2014.05.003. Epub 2014 May 22.
With the widespread use of time-lapse data to understand cellular function, there is a need for tools which facilitate high-throughput analysis of data. Fluorescence microscopy of genetically engineered cell lines in culture can be used to visualise the progression of these cells through the cell cycle, including distinctly identifiable sequential stages of cell division (mitotic phases). We present a system for automated segmentation and mitotic phase labelling using temporal models. This work takes the novel approach of using temporal features evaluated over the whole of the mitotic phases rather than over single frames, thereby capturing the distinctive behaviour over the phases. We compare and contrast three different temporal models: Dynamic Time Warping, Hidden Markov Models, and Semi Markov Models. A new loss function is proposed for the Semi Markov model to make it more robust to inconsistencies in data annotation near transition boundaries. The models are tested under two different experimental conditions to explore robustness to changes in biological conditions.
随着延时数据在理解细胞功能方面的广泛应用，我们需要一些工具来帮助实现数据的高通量分析。通过对培养的基因工程细胞系进行荧光显微镜观察，可以可视化这些细胞通过细胞周期的过程，包括可明显识别的细胞分裂的顺序阶段（有丝分裂阶段）。我们提出了一种使用时间模型进行自动分割和有丝分裂阶段标记的系统。这项工作采用了一种新颖的方法，即在整个有丝分裂阶段而不是单个帧上评估时间特征，从而捕捉到阶段之间的独特行为。我们比较和对比了三种不同的时间模型：动态时间规整、隐马尔可夫模型和半马尔可夫模型。针对半马尔可夫模型提出了一种新的损失函数，使其在过渡边界附近的数据注释不一致时更具鲁棒性。在两种不同的实验条件下对模型进行了测试，以探索对生物条件变化的稳健性。
