Viability diagnosis in biotechnological cultures through image processing

This work evaluates the possibility of measuring the biomass concentration and diagnosing the cell viability in non-stained cultures of yeast cells through image processing techniques. The algorithm presented in this study is validated on Saccharomyces cerevisiae cells. It processes the images acquired off-line on a bright field microscope, in order to enhance the features of the cells, to assess the viability and to estimate the biomass concentration. The enhancement method consists of the following operations: image contrast adjustment, noise reduction, image segmentation, cell recognition and labeling, determination of biomass concentration, recognition and counting of living cells, determining of the biomass concentration. The feature enhancement helps the human operator to better see the living cells, but the viability and concentration can be estimated independent of the operator. The results were validated through phase-contrast microscopy.

[1]  R. Jones.  Measures of yeast death and deactivation and their meaning. I , 1987 .

[2]  Tim Wilhelm Nattkemper,et al.  An in situ probe for on‐line monitoring of cell density and viability on the basis of dark field microscopy in conjunction with image processing and supervised machine learning , 2007, Biotechnology and bioengineering.

[3]  Thomas Scheper,et al.  A review of non-invasive optical-based image analysis systems for continuous bioprocess monitoring , 2010, Bioprocess and biosystems engineering.

[4]  D. Veal,et al.  A flow-cytometric method for determination of yeast viability and cell number in a brewery. , 2003, FEMS yeast research.

[5]  L. Castro-Concha,et al.  Measurement of cell viability. , 2012, Methods in molecular biology.

[6]  N. Mock,et al.  An improved method for monitoring cell death in cell suspension and leaf disc assays using evans blue , 1994, Plant Cell, Tissue and Organ Culture.

[7]  Tim Wilhelm Nattkemper,et al.  In situ dark field microscopy for on-line monitoring of yeast cultures , 2007, Biotechnology Letters.

[8]  T. Nattkemper,et al.  Identification of Cell Viability by an In-situ Dark-fieldMicroscopic Sensor , 2005 .

[9]  Feihong Yu,et al.  A Method of Automatic Cell Counting Based on Microscopic Image , 2013, 2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics.

[10]  J. S. Guez,et al.  The viability of animal cell cultures in bioreactors: Can it be estimated online by using in situ microscopy? , 2010 .

[11]  J. Widholm.  The use of fluorescein diacetate and phenosafranine for determining viability of cultured plant cells. , 1972, Stain technology.

[12]  F. Vázquez-Flota,et al.  Plant Cell Culture Protocols , 2005, Methods in Molecular Biology.

[13]  Jayme Garcia Arnal Barbedo,et al.  A Review on Methods for Automatic Counting of Objects in Digital Images , 2012 .

[14]  T. Nattkemper,et al.  CellViCAM--Cell viability classification for animal cell cultures using dark field micrographs. , 2010, Journal of biotechnology.

[15]  P. Wiedemann,et al.  On-line and real time cell counting and viability determination for animal cell process monitoring by in situ microscopy , 2011, BMC proceedings.