Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning

Mirsky, Simcha K.; Barnea, Itay; Levi, Mattan; Greenspan, Hayit; Shaked, Natan T.

doi:10.1002/cyto.a.23189

Cited by 78 publications

(58 citation statements)

References 20 publications

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“…Machine learning results suggest 200 cells per group to be an adequate sample size for classification. Other similar studies have also found this number of cells aids in cell line classification (7,15,44). Confluency was constant for the machine learning dataset, and when varied, affected numerous phase parameters (Fig.…”

Section: Discussionsupporting

confidence: 54%

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

et al. 2019

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Robust and reproducible profiling of cell lines is essential for phenotypic screening assays. The goals of this study were to determine robust and reproducible optical phase signatures of cell lines for classification with machine learning and to correlate optical phase parameters to motile behavior. Digital holographic microscopy (DHM) reconstructed phase maps of cells from two pairs of cancer and non‐cancer cell lines. Seventeen image parameters were extracted from each cell's phase map, used for linear support vector machine learning, and correlated to scratch wound closure and Boyden chamber chemotaxis. The classification accuracy was between 90% and 100% for the six pairwise cell line comparisons. Several phase parameters correlated with wound closure rate and chemotaxis across the four cell lines. The level of cell confluence in culture affected phase parameters in all cell lines tested. Results indicate that optical phase features of cell lines are a robust set of quantitative data of potential utility for phenotypic screening and prediction of motile behavior. © 2019 International Society for Advancement of Cytometry

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Section: Discussionsupporting

confidence: 54%

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

et al. 2019

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“…The isolation and evaluation of sperm cells were performed using a MATLAB algorithm that our group had previously designed for this purpose, and was fully presented in Ref. .…”

Section: Methodsmentioning

confidence: 99%

Stain‐free interferometric phase microscopy correlation with DNA fragmentation stain in human spermatozoa

Barnea

Karako

Mirsky

et al. 2018

Journal of Biophotonics

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Acridine orange (AO) staining is used to diagnose DNA fragmentation status in sperm cells. Interferometric phase microscopy (IPM) is an optical imaging method based on digital holographic microscopy that provides quantitative morphological and refractive index imaging of cells in vitro without the need for staining. We have imaged sperm cells using stain-free IPM in order to estimate different cellular parameters, such as acrosome dry mass and size, in addition to an embryologist evaluation according to the World Health Organization (WHO)-2010 criteria. Following this, the same sperm cells were stained by AO, imaged using a fluorescence confocal microscope and assessed by the AO-emitted color, forming five DNA fragmentation groups. These DNA fragmentation groups were correlated with the embryologist-based classification and the IPM-based morphological parameters. Our results indicate on significant differences in the IPM-based parameters between groups with different fragmentation levels. Based on the validation with AO, we conclude that stain-free IPM images analyzed digitally may assist in selecting sperm cells with intact DNA prior to intracytoplasmic sperm injection. This information may potentially increase percentage of successful pregnancies.

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“…In other words, considering the proposed H‐SVM as a binary classifier, we are able to identify exactly MPs in pretreated seawater, thus discarding the other objects falling within the same range of characteristic scales. Previous works have proposed the use of holographic reconstructions to classify particles, cells, or microorganisms based on statistical methods or ML architectures . However, none of the existing ML‐DH approaches have tackled the problem of identifying MPs, which have their own specificity as the MP class consists of a wide heterogeneity of materials, morphologies, and characteristic scales.…”

Section: Discussionmentioning

confidence: 99%

Microplastic Identification via Holographic Imaging and Machine Learning

Bianco

Memmolo

Carcagnì

et al. 2020

Advanced Intelligent Systems

108

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Microplastics (MPs) are a major environmental concern due to their possible impact on water pollution, wildlife, and the food chain. Reliable, rapid, and high‐throughput screening of MPs from other components of a water sample after sieving and/or digestion is still a highly desirable goal to avoid cumbersome visual analysis by expert users under the optical microscope. Here, a new approach is presented that combines 3D coherent imaging with machine learning (ML) to achieve accurate and automatic detection of MPs in filtered water samples in a wide range at microscale. The water pretreatment process eliminates sediments and aggregates that fall out of the analyzed range. However, it is still necessary to clearly distinguish MPs from marine microalgae. Here, it is shown that, by defining a novel set of distinctive “holographic features,” it is possible to accurately identify MPs within the defined analysis range. The process is specifically tailored for characterizing the MPs' “holographic signatures,” thus boosting the classification performance and reaching accuracy higher than 99% in classifying thousands of items. The ML approach in conjunction with holographic coherent imaging is able to identify MPs independently from their morphology, size, and different types of plastic materials.

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Automated analysis of individual sperm cells using stain‐free interferometric phase microscopy and machine learning

Cited by 78 publications

References 20 publications

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

Machine Learning with Optical Phase Signatures for Phenotypic Profiling of Cell Lines

Stain‐free interferometric phase microscopy correlation with DNA fragmentation stain in human spermatozoa

Microplastic Identification via Holographic Imaging and Machine Learning

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