Real-time stain-free classification of cancer cells and blood cells using interferometric phase microscopy and machine learning

TL;DR

This paper introduces a real-time, stain-free method combining interferometric phase microscopy and machine learning to classify cancer and blood cells during flow cytometry at 15 cells/sec, achieving over 92% accuracy.

Contribution

It presents a novel, rapid, label-free classification technique for cancer and blood cells using digital holographic microscopy and machine learning in flow cytometry.

Findings

Achieved 92.56% accuracy in cell classification.

Demonstrated real-time processing at 15 cells per second.

Successfully distinguished primary and metastatic colon cancer cells.

Abstract

We present a method for a real time visualization and automatic processing for detection and classification of untouched cancer cells in blood during stain free imaging flow cytometry using digital holographic microscopy and machine learning in throughput of 15 cells per second. As a preliminary model for circulating tumor cells in blood, we automatically classified primary and metastatic colon cancer cells, where the two types of cancer cells were isolated from the same individual, as well as four types of blood cells. We used low-coherence off-axis interferometric phase microscopy and a microfluidic channel to quantitatively image cells during flow. The acquired images were processed and classified based on their morphology and quantitative phase features during the cell flow. We achieved high accuracy of 92.56 percent for distinguishing between the cells, paving the way for future automatic label-free cancer cell classification in blood samples.