Cell Motility Dynamics: A Novel Segmentation Algorithm to Quantify Multi-Cellular Bright Field Microscopy Images

Confocal microscopy analysis of fluorescence and morphology is becoming the standard tool in cell biology and molecular imaging. Accurate quantification algorithms are required to enhance the understanding of different biological phenomena. We present a novel approach based on image-segmentation of multi-cellular regions in bright field images demonstrating enhanced quantitative analyses and better understanding of cell motility. We present MultiCellSeg, a segmentation algorithm to separate between multi-cellular and background regions for bright field images, which is based on classification of local patches within an image: a cascade of Support Vector Machines (SVMs) is applied using basic image features. Post processing includes additional classification and graph-cut segmentation to reclassify erroneous regions and refine the segmentation. This approach leads to a parameter-free and robust algorithm. Comparison to an alternative algorithm on wound healing assay images demonstrates its superiority. The proposed approach was used to evaluate common cell migration models such as wound healing and scatter assay. It was applied to quantify the acceleration effect of Hepatocyte growth factor/scatter factor (HGF/SF) on healing rate in a time lapse confocal microscopy wound healing assay and demonstrated that the healing rate is linear in both treated and untreated cells, and that HGF/SF accelerates the healing rate by approximately two-fold. A novel fully automated, accurate, zero-parameters method to classify and score scatter-assay images was developed and demonstrated that multi-cellular texture is an excellent descriptor to measure HGF/SF-induced cell scattering. We show that exploitation of textural information from differential interference contrast (DIC) images on the multi-cellular level can prove beneficial for the analyses of wound healing and scatter assays. The proposed approach is generic and can be used alone or alongside traditional fluorescence single-cell processing to perform objective, accurate quantitative analyses for various biological applications.

荧光与形态学的共聚焦显微镜（Confocal microscopy）分析已逐渐成为细胞生物学与分子成像领域的标准研究手段。为深化对各类生物现象的认知，亟需精准的量化分析算法。我们提出了一种基于明场图像多细胞区域图像分割的全新方法，可实现更精准的定量分析，加深对细胞运动的理解。本文介绍了MultiCellSeg：一款面向明场图像的多细胞区域与背景分离分割算法，其核心是对图像内的局部图像块进行分类：采用基于基础图像特征的级联支持向量机（Support Vector Machines，SVMs）完成分类。后续处理步骤包含额外分类与图割（graph-cut）分割操作，用于重新归类错误区域并优化分割结果。该方法最终得到一款无参数且鲁棒性优异的算法。通过与其他同类算法在划痕愈合实验（wound healing assay）图像上的对比测试，证明了本算法的优越性。
本方法被用于评估常见的细胞迁移模型，例如划痕愈合实验与散射实验（scatter assay）。我们将其应用于量化肝细胞生长因子/散射因子（Hepatocyte growth factor/scatter factor，HGF/SF）对延时共聚焦显微镜划痕愈合实验中细胞愈合速率的加速效应，实验结果表明，处理组与对照组细胞的愈合速率均呈线性关系，且HGF/SF可将愈合速率提升约一倍。我们还开发了一种全新的全自动、精准且零参数的方法，用于对散射实验图像进行分类与评分，实验证明多细胞纹理是衡量HGF/SF诱导的细胞散射的优质特征描述符。研究表明，在多细胞层面利用微分干涉差（differential interference contrast，DIC）图像的纹理信息，可有效助力划痕愈合与散射实验的分析工作。本方法具有通用性，既可单独使用，也可与传统的荧光单细胞处理流程结合，为各类生物应用场景提供客观精准的定量分析支持。