Fenistein D, Lenseigne B, Christophe T, Brodin P, Genovesio A
Image Mining Group, Institut Pasteur Korea, Hawolgok-dong, Seongbuk-gu, Seoul 136-791, Korea.
Cytometry A. 2008 Oct;73(10):958-64. doi: 10.1002/cyto.a.20627.
High-throughput, high-content screening (HT-HCS) of large compound libraries for drug discovery imposes new constraints on image analysis algorithms. Time and robustness are paramount while accuracy is intrinsically statistical. In this article, a fast and fully automated algorithm for cell segmentation is proposed. The algorithm is based on a strong attachment to the data that provide robustness and have been validated on the HT-HCS of large compound libraries and different biological assays. We present the algorithm and its performance, a description of its advantages and limitations, and a discussion of its range of application.
用于药物发现的大型化合物库的高通量、高内涵筛选（HT-HCS）对图像分析算法提出了新的限制。时间和稳健性至关重要，而准确性本质上是统计学的。本文提出了一种用于细胞分割的快速且全自动的算法。该算法基于对数据的紧密依附，从而提供稳健性，并且已经在大型化合物库的HT-HCS和不同生物学检测中得到验证。我们展示了该算法及其性能，描述了其优缺点，并讨论了其应用范围。
