In vitro monitoring of photoinduced necrosis in HeLa cells using digital holographic microscopy and machine learning

Abstract

Digital holographic microscopy supplemented with the developed cell segmentation and machine learning and classification algorithms is implemented for quantitative description of the dynamics of cellular necrosis induced by photodynamic treatment in vitro. It is demonstrated that the developed algorithms operating with a set of optical, morphological, and physiological parameters of cells, obtained from their phase images, can be used for automatic distinction between live and necrotic cells. The developed classifier provides high accuracy of about 95.5% and allows for calculation of survival rates in the course of cell death.

Perspectives on liquid biopsy for label‐free detection of “circulating tumor cells” through intelligent lab‐on‐chips

“…Although the human capability of delivering large volumes of clinically relevant data exponentially increased over the last decade, the capacity of effectively analyzing such data did not, being naturally limited by the skills of the pathologists called to judge based on their own experience. Thus, biology research, diagnostics, and medicine naturally started relying on AI‐based cellular image analysis 147‐186 . AI largely extends the variety of tasks that image analysis can accomplish.…”

“…One main distinction can be made between “classical” machine learning and “deep learning.” The former typically exploits features engineering to learn from descriptors of each element of the dataset. Expert users define a set of image‐based features that are supposed to be distinctive enough to allow separating different populations in a proper feature subspace 17,18,147‐150 . The analysis of the Pearson autocorrelation matrix is of help to reduce redundant dimensions 150 .…”

“…Kinetic states classification in mammalian cell lines has been carried out using machine learning to identify rare events such as therapeutic resistance or transition events like cells differentiation 150 . Photoinduced necrosis of HeLa cells has been also studied relying on DH microscopy and machine learning 149 . Remarkably, smart computational cytometers have been presented, which use different coherent imaging schemes and deep learning to screen and sort out rare cells automatically with high throughput.…”

Neuroblastoma Cells Classification Through Learning Approaches by Direct Analysis of Digital Holograms

“…A well-established analysis method to classify cells phenotyping relies on the joint use of features engineering and suitable classifiers, thus implementing conventional Machine Learning (ML) paradigms. This has been exploited for blood cells characterization [4], sick cells identification [5], marine micro-organisms identification [3], just to name a few. Recently, a sub-class of ML, namely Deep Learning, has gained credits as the elective approach for advanced image analysis in microscopy [6], [7], [8], [9], [10], [11], [12].…”

Recognition of Multiscale Dense Gel Filament-Droplet Field in Digital Holography With Mo-U-Net

“…But until now, these works mainly focus on relatively large [33,34], spherical object [35,36], sparse small particle field [37][38][39] or other objects (e.g., fiber internal structure [40], cell identification [41]), yet few focused on dense particle field consisted of liquid droplets and filaments with various morphological shapes like gel atomization field. And the combination of digital holography and deep learning methods were also extended to other particle-like objects, Belashov, et al [42] utilized holographic microscopy combined with cell segmentation algorithm using machine learning to characterize the dynamic process of apoptosis and the accuracy achieved 95.5% and Wang, et al [43] segmented some terahertz images of gear wheel and used average structural similarity to get the relatively best results which were proved to be better than some traditional segmentation algorithms in their paper.…”