Abstract
In the last few years, classification of cells by machine learning has become frequently used in biology. However, most of the approaches are based on morphometric (MO) features, which are not quantitative in terms of cell mass. This may result in poor classification accuracy. Here, we study the potential contribution of coherence-controlled holographic microscopy enabling quantitative phase imaging for the classification of cell morphologies. We compare our approach with the commonly used method based on MO features. We tested both classification approaches in an experiment with nutritionally deprived cancer tissue cells, while employing several supervised machine learning algorithms. Most of the classifiers provided higher performance when quantitative phase features were employed. Based on the results, it can be concluded that the quantitative phase features played an important role in improving the performance of the classification. The methodology could be valuable help in refining the monitoring of live cells in an automated fashion. We believe that coherence-controlled holographic microscopy, as a tool for quantitative phase imaging, offers all preconditions for the accurate automated analysis of live cell behavior while enabling noninvasive label-free imaging with sufficient contrast and high-spatiotemporal phase sensitivity.
Highlights
In many fields of biology, such as cancer research, drug discovery,[2,3] cell death,[4] phenotypic screening,[5] study of pathological processes,[6,7] or interactions of cells with biomaterials,[8] the microscopy study of cell morphology belongs to essential research methods
Viable cells did not exhibit any changes in morphology; cells in the semideprived category were influenced by phosphate-buffered saline (PBS) and started to shrink while their boundaries became indented
Even though we have presented the application of quantitative phase imaging together with the supervised classification only for distinguishing different morphologies of deprived cells, the approach might contribute to higher performances when it comes to different classification tasks
Summary
In many fields of biology, such as cancer research, drug discovery,[2,3] cell death,[4] phenotypic screening,[5] study of pathological processes,[6,7] or interactions of cells with biomaterials,[8] the microscopy study of cell morphology belongs to essential research methods. Manual observation and evaluation of cell morphology in microscopy images require a trained biologist who performs inspection on every image. The increasing prevalence of automated image acquisition systems is enabling microscopy experiments that generate large image datasets. Manual image analysis becomes rather time-consuming and requires considerable effort and concentration of the investigator. The analysis provided by one person has a tendency to be biased by subjective observation. The analysis results, largely depend on personal skills, decisions, and preferences. These aspects impose significant constraints on the speed and reliability of cell morphology evaluation from microscopy images
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