  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.
