Unsupervised classification from defocused holographic images for label-free HEK293 cell culture viability assessment

This M.Tech project aims to create an automated, label-free technique for evaluating the viability and number of HEK293 cell cultures using defocused digital holography and to establish a methodology for automated, label-free assessment of HEK293 cell culture viability and concentration using defocused digital holography. The methodology capitalizes on the imaging capabilities of the phase imaging method to monitor certain critical parameters of the bioproduction process while keeping the cost of production at an acceptable level. A central aspect of the project is the development of an off-line image processing pipeline that reconstructs trilambda holograms to determine, within the bounds of the parameters set, cell concentration and viability estimates compared to

the established parameters from Vi-Cell measurements, including the absence of the need for cell labeling. The proposed algorithmic framework that incorporates supervised and unsupervised learning illustrates the degree of applicability with different HEK293 cell clones and different time intervals without the need for extensive parameter adjustments. In addition, the framework is an exploratory project that assesses the applicability of features of images obtained from one experimental set to separate cell cultures, enabling predictive analysis. Comparative analysis demonstrated that single wavelength holograms, while slightly improving predictive capacity, were not as effective as the use of RGB in holography, which resulted in more discriminative feature representations and improved clustering. The present study

attempts to address the question of the consequences of feature reduction, concluding that limiting image features to the most critical 50 % of features produced only a slight decrease in the accuracy of the assessment.

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