Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification

Investigation of the multiple characteristics of the self-mixing effect subject to a single particle

As a compact interferometry technique, self-mixing interferometry (SMI) is a promising tool for micro particle detection in biochemical analysis and the monitoring of laser manufacturing processing, and currently SMI based micro particle detection is attracting increasing attention. However, unlike the typical displacement or vibration measurement driven by a macro target, only a small amount of literature has targeted the SMI effect induced by a single micro moving particle. In this paper, two numerical models were investigated to describe the characteristics of the signal sparked by individual particle. We compared the measurement results with the two models' simulations in three signal characteristic aspects: the temporal waveform, frequency spectrum, and phase profile. From these results, we established that both amplitude modulation and frequency modulation effects apply under different conditions in the self-mixing process. And for the first time, we analyzed the effect of the laser illumination spot size on the particle-induced SMI signal features with two optical arrangements. When the laser beam size is larger than the particle size, the signal bursts are likely to result from frequency modulation, and vice versa. Our results can improve the capability of SMI technology in particle size discrimination and particle sorting.

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

Real-Time Stain-Free Classification of Cancer Cells and Blood Cells Using Interferometric Phase Microscopy and Machine Learning

We present a method for real-time visualization and automatic processing for detection and classification of untreated 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 the blood, following an initial label-free rapid enrichment stage based on the cell size, we applied our holographic imaging approach, providing the quantitative optical thickness profiles of the cells during flow. 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 image cells during flow quantitatively. 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% for distinguishing between the cells, enabling further automatic enrichment and cancer-cell grading from blood. © 2020 International Society for Advancement of Cytometry.