Unsupervised organization of cervical cells using bright‐field and single‐shot digital holographic microscopy

Abstract

We report results on unsupervised organization of cervical cells using microscopy of Pap-smear samples in brightfield (3-channel color) as well

Quantitative scoring of epithelial and mesenchymal qualities of cancer cells using machine learning and quantitative phase imaging

“…In single-cell studies, phase images of cells of interest could guide laser-capture microdissection to link observed behavior, morphology, and gene expression at a single cell level. Indeed, advanced machine learning techniques, including deep learning, 29 have recently been applied to isolate cell subpopulations based on unique phase features 6 and other phenotypic differences, 66,37 including metastatic versus primary cancer 67 and different types of nonactivated lymphocytes. 68 The phase/morphology score concept described here could be applied to support decision-making in intelligent cell sorting systems, such as flow cytometry with QPI, 69,34 to partition cells from a heterogeneous population into distinct morphological groups.…”

“…28,29 The effects of cell seeding density, 4 exposure to anticancer drugs, 30,31 and other influences on cell phenotype [32][33][34] have been robustly evaluated with QPI. Quantitative imaging and machine learning have the potential to save time, labor, and reduce human error in phenotypic profiling, which could help pathologists and scientists to accurately detect circulating tumor cells, 35 classify cancer cells, 36,37 evaluate the metastatic potential of cancer cells, 38 and assess cancer drug resistance. 39 Thus, machine learning-assisted QPI has great power to aid in interpreting large-scale and high-dimensionality data from cells, potentially enhancing cancer diagnosis and treatment.…”

Cytopathology Using High Resolution Digital Holographic Microscopy

“…Here * represents the complex conjugation of the corresponding wave-function. In the image plane holography case as in the present study, Ox , y ðÞ represents the resultant image field corresponding to the cell sample slide when observed through a40x infinity corrected imaging system [23]. The interference is possible due to the use of a laser source which ensures that the object and reference waves remain temporally coherent at the detector plane and produce interference fringes with good contrast.…”

“…The first term of the cost function represents the least square data fit and the second term ψ O, O * ðÞ is a suitable image domain constraint. We use the modified Huber penalty function as a constraint and use an adaptive alternating minimization scheme explained in detail elsewhere [23,26] for recovering the complex object function Ox , y ðÞ in the image plane. The modified Huber penalty is defined as:…”

“…This way the clinicians can correlate with their traditional knowledge and treat phase images as an additional channel of morphological information. Recently we demonstrated such an approach for unsupervised organization of cervical cell images [23]. In the present imaging study over a much larger sample size with samples collected from different clinical sites, we examine the structural changes in phase images of cervical cell nuclei and highlight their potential importance for cell classification.…”