Automated classification of cell morphology by coherence-controlled holographic microscopy

Automated classification of cell morphology by coherence-controlled holographic microscopy

WoS Article

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 coherencecontrolled 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.

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 coherencecontrolled 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.

coherence-controlled holographic microscopy; digital holographic microscopy; quantitative phase imaging; supervised machine learning; classification; cell morphology

coherence-controlled holographic microscopy; digital holographic microscopy; quantitative phase imaging; supervised machine learning; classification; cell morphology

ŠTRBKOVÁ, L.; ZICHA, D.; VESELÝ, P.; CHMELÍK, R.

2018

23.08.2017

SPIE

Bellingham WA 98227-0010 USA

1083-3668

JOURNAL OF BIOMEDICAL OPTICS

22

8

United States of America

086008-1

086008-9

9

http://dx.doi.org/10.1117/1.JBO.22.8.086008

http://hdl.handle.net/11012/84156

086008_1