Becker T, Madany A
Graduate School for Computing in Medicine and Life Science, University of Lübeck, Ratzeburger Allee 16023538 Lübeck, Germany.
Methods Inf Med. 2012;51(5):449-56. doi: 10.3414/ME11-02-0038. Epub 2012 Aug 31.
The cultivation of adherently growing cell populations is a major task in the field of adult stem cell production used for drug discovery and in the field of regenerative medicine. To assessthe quality of a cell population, a crucial event is the mitotic cell division: the precise knowledge of these events enables the reconstruction of lineages and accurate proliferation curves as well as a detailed analysis of cell cycles. To serve in an autonomous cell farming framework, such a detector requires to work reliably and unsupervised.
We introduce a mitosis detector that is using a maximum likelihood (ML) estimator based on morphological cell features (cell area, brightness, length, compactness). It adapts to the 3 phases of cell growth (lag, log and stationary phase). As a concurrent model, we compared ML with kernel SVMs using linear, quadratic and Gaussian kernel functions. All approaches are evaluated for their ability to distinguish between mitotic and non-mitotic events. The large, publicly available benchmark data CeTReS (reference data set A with >240,000 segmented cells, >2,000 mitotic events) is used for this evaluation.
The adaptive (unsupervised) ML approach clearly outperforms previously published non-adaptive approaches and the linear SVM. Furthermore, it robustly reaches a performance comparable to quadratic and Gaussian SVM.
The proposed simple and label free adaptive variant might be the method of choice when it comes to autonomous cell farming. Hereby, it is essential to have reliable and unsupervised mitosis detection that covers all phases of cell growth.
培养贴壁生长的细胞群体是用于药物发现的成体干细胞生产领域以及再生医学领域的一项主要任务。为评估细胞群体的质量，一个关键事件是有丝分裂细胞分裂：对这些事件的精确了解能够重建细胞谱系、绘制准确的增殖曲线以及详细分析细胞周期。为在自主细胞培养框架中发挥作用，这样的检测器需要可靠且无监督地工作。
我们引入一种有丝分裂检测器，它基于细胞形态特征（细胞面积、亮度、长度、紧凑度）使用最大似然（ML）估计器。它能适应细胞生长的三个阶段（延迟期、对数期和稳定期）。作为并行模型，我们将ML与使用线性、二次和高斯核函数的核支持向量机（SVM）进行了比较。所有方法都针对区分有丝分裂和非有丝分裂事件的能力进行了评估。使用大型公开基准数据集CeTReS（参考数据集A，有超过240,000个分割细胞，超过2,000个有丝分裂事件）进行此评估。
自适应（无监督）ML方法明显优于先前发表的非自适应方法和线性SVM。此外，它稳健地达到了与二次和高斯SVM相当的性能。
当涉及自主细胞培养时，所提出的简单且无标记的自适应变体可能是首选方法。因此，拥有可靠且无监督的有丝分裂检测以涵盖细胞生长的所有阶段至关重要。
