基于主成分的图像分割：一种体外细胞集落轮廓的新方法
Principal component-based image segmentation: a new approach to outline in vitro cell colonies
摘要：
体外克隆形成试验是研究细胞在培养皿中形成集落的能力的技术。通过光学成像，可以对带有染色菌落的培养皿进行数字化扫描和评估。染色菌落的识别、分割和计数在生物检测的高通量筛选和定量评估中起着至关重要的作用。这种成像/扫描测定的图像处理可能受到图像/扫描采集伪像（如背景噪声和空间变化的照明）以及悬浮介质中的污染物的影响。虽然现有的方法解决了这些问题，分割质量需要进一步提高，特别是在嘈杂和低对比度的图像。在这项工作中，我们提出了一个客观的和通用的机器学习程序来修改这些问题的特征，提取和分割查询殖民地使用主成分分析，k-均值聚类和修改后的分水岭分割算法。其目的是通过空间纹理评估自动识别可见菌落，并相应地将其与背景区分开来，为后续分割做准备。所提出的分割算法产生了类似的质量人工计数的人类观察员。高F1分数（>0.9）和低均方根误差（约14%）强调了与地面真实数据的良好一致性。此外，它优于最近的最先进的方法。该方法将成为未来癌症研究应用的重要工具。
英文摘要：
The in vitro clonogenic assay is a technique to study the ability of a cell to form a colony in a culture dish. By optical imaging, dishes with stained colonies can be scanned and assessed digitally. Identification, segmentation and counting of stained colonies play a vital part in high-throughput screening and quantitative assessment of biological assays. Image processing of such pictured/scanned assays can be affected by image/scan acquisition artifacts like background noise and spatially varying illumination, and contaminants in the suspension medium. Although existing approaches tackle these issues, the segmentation quality requires further improvement, particularly on noisy and low contrast images. In this work, we present an objective and versatile machine learning procedure to amend these issues by characterizing, extracting and segmenting inquired colonies using principal component analysis, k-means clustering and a modified watershed segmentation algorithm. The intention is to automatically identify visible colonies through spatial texture assessment and accordingly discriminate them from background in preparation for successive segmentation. The proposed segmentation algorithm yielded a similar quality as manual counting by human observers. High F1 scores (>0.9) and low root-mean-square errors (around 14%) underlined good agreement with ground truth data. Moreover, it outperformed a recent state-of-the-art method. The methodology will be an important tool in future cancer research applications.
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