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17 October 2006 Colony scoring in hematotoxicity assays by an image stack classifier: first results
Giovanni Franco Crosta, Laura Fumarola, Ilaria Malerba, Laura Gribaldo M.D.
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Proceedings Volume 6386, Optical Methods in the Life Sciences; 63860A (2006) https://doi.org/10.1117/12.685110
Event: Optics East 2006, 2006, Boston, Massachusetts, United States
Abstract
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). These assays require live and unstained colonies to be counted. Most laboratories still rely on visual scoring, which 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 dtat from a whole stack.
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Giovanni Franco Crosta, Laura Fumarola, Ilaria Malerba, and Laura Gribaldo M.D. "Colony scoring in hematotoxicity assays by an image stack classifier: first results", Proc. SPIE 6386, Optical Methods in the Life Sciences, 63860A (17 October 2006); https://doi.org/10.1117/12.685110
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KEYWORDS
Image segmentation
In vitro testing
Image processing
Algorithm development
Detection and tracking algorithms
Bone
3D image processing
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