Colony scoring in hematotoxicity assays by an image stack classifier: First results

摘要

In order to evaluate the potential hematotoxicity of xenobiotics, including candidate anti-cancer drugs, in vitro models of hematopoiesis are used, which involve clonogenic assays on CFU-GM (Colony Forming Unit-Granulocyte-Macrophage) progenitors. These assays require live and unstained colonies to be counted. Colony scoring, which is performed visually by highly trained experts, is time consuming and error prone. As a consequence automated scoring is highly desired. A classification algorithm aimed at emulating the colony recognition and scoring capabilities of a human expert has been developed. A first account will be given herewith. Assays were carried out on CFU-GM progenitors derived from human umbilical cord blood cells and grown in methylcellulose. A three-dimensional (3-D) medium is essential for these assays to simulate the clonogenetic process which takes place in bone marrow. Stacks of images representing slices of a 3-D domain were acquired. Structure and texture information was extracted from each image. Classifier training was based on a 3-D colony model applied to the image stack. The number of scored colonies (assigned class) was required to match the count supplied by the human expert (class of belonging). Successful applications to scoring colonies, which partially overlap and/or are masked by caustics, are described. Whereas the industry's scoring methods all rely on image structure alone and process 2-D data, the classifier described herewith takes texture into account and fuses 3-D data from a whole stack.