Convolutional neural network based image segmentation: a review

Ajmal, Hina; Rehman, Saad; Farooq, Umar; Ain, Qurrat Ul; Riaz, Farhan; Hassan, Ali

doi:10.1117/12.2304711

Cited by 18 publications

(11 citation statements)

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“…Conversely, M(IL4/IL13) macrophages showed greater increases in redox ratio than M(IFN-γ) macrophages with inhibition of oxidative phosphorylation and fatty acid oxidation ( Fig 1E). This is consistent with prior studies that show increased reliance on oxidative phosphorylation and fatty acid oxidation in M2-like macrophages compared to M1-like macrophages [14][15][16][17][18][19] . Fold changes are statistically significant between control and inhibitor-treated conditions across all treatments ( Supplementary Table 1).…”

Section: Metabolic Imaging Validation: Macrophage Stimulation In 2d Isupporting

confidence: 93%

“…This increases the production of the metabolic co-enzyme, NADH, via glycolysis and sustains the viability of M1-like macrophages within tumors [14][15][16] . Conversely, elevated fatty acid oxidation and oxidative phosphorylation in M2-like macrophages support angiogenesis and tumor growth [14][15][16][17][18][19] . Here, fatty acid oxidation generates acetyl-CoA and NADH, driving downstream oxidative phosphorylation 17,19 .…”

Section: Introductionmentioning

confidence: 99%

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Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism

Heaster

Humayun

et al. 2020

Preprint

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Macrophages within the tumor microenvironment (TME) exhibit a spectrum of pro-tumor and antitumor functions, yet it is unclear how the TME regulates this macrophage heterogeneity. Standard methods to measure macrophage heterogeneity require destructive processing, limiting spatiotemporal studies of function within the live, intact 3D TME. Here, we demonstrate twophoton autofluorescence imaging of NAD(P)H and FAD to non-destructively resolve spatiotemporal metabolic heterogeneity of individual macrophages within 3D microscale TME models. Fluorescence lifetimes and intensities of NAD(P)H and FAD were acquired at 24, 48, and 72 hours post-stimulation for mouse macrophages (RAW 264.7) stimulated with IFN-γ or IL-4 plus IL-13 in 2D culture, validating that autofluorescence measurements capture known metabolic phenotypes. To quantify metabolic dynamics of macrophages within the TME, mouse macrophages or human monocytes (RAW264.7 or THP-1) were cultured alone or with breast cancer cells (mouse PyVMT or primary human IDC) in 3D microfluidic platforms. Human monocytes and mouse macrophages in tumor co-cultures exhibited significantly different FAD mean lifetimes and greater migration than mono-cultures at 24, 48, and 72 hours post-seeding. In co-cultures with primary human cancer cells, actively-migrating monocyte-derived macrophages had greater redox ratios (NAD(P)H/FAD intensity) compared to passively-migrating monocytes at 24 and 48 hours post-seeding, reflecting metabolic heterogeneity in this subpopulation of monocytes. Genetic analyses further confirmed this metabolic heterogeneity. These results establish label-free autofluorescence imaging to quantify dynamic metabolism, polarization, and migration of macrophages at single-cell resolution within 3D microscale models. This combined culture and imaging system provides unique insights into spatiotemporal tumorimmune crosstalk within the 3D TME.

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Section: Metabolic Imaging Validation: Macrophage Stimulation In 2d Isupporting

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism

Heaster

Humayun

et al. 2020

Preprint

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“…Briefly, CNNs downsample recorded images through kernel convolution and window pooling steps, resulting in a low-resolution image on which predictions of pixel class membership are made (i.e., contraction). 202 Pixel positions from the initial pooling steps are recalled to assign class predictions to pixels in upsampled images (i.e., expansion). 202 This computational structure has been used to analyze hyperspectral fluorescence lifetime images and to dynamically monitor fluorescence lifetimes in vitro and in vivo [ Fig.…”

Section: Machine Learning Analysismentioning

confidence: 99%

“…202 Pixel positions from the initial pooling steps are recalled to assign class predictions to pixels in upsampled images (i.e., expansion). 202 This computational structure has been used to analyze hyperspectral fluorescence lifetime images and to dynamically monitor fluorescence lifetimes in vitro and in vivo [ Fig. 6(c)].…”

Section: Machine Learning Analysismentioning

confidence: 99%

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Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

et al. 2020

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Significance: Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique to distinguish the unique molecular environment of fluorophores. FLIM measures the time a fluorophore remains in an excited state before emitting a photon, and detects molecular variations of fluorophores that are not apparent with spectral techniques alone. FLIM is sensitive to multiple biomedical processes including disease progression and drug efficacy. Aim: We provide an overview of FLIM principles, instrumentation, and analysis while highlighting the latest developments and biological applications. Approach: This review covers FLIM principles and theory, including advantages over intensitybased fluorescence measurements. Fundamentals of FLIM instrumentation in time-and frequencydomains are summarized, along with recent developments. Image segmentation and analysis strategies that quantify spatial and molecular features of cellular heterogeneity are reviewed. Finally, representative applications are provided including high-resolution FLIM of cell-and organelle-level molecular changes, use of exogenous and endogenous fluorophores, and imaging protein-protein interactions with Förster resonance energy transfer (FRET). Advantages and limitations of FLIM are also discussed. Conclusions: FLIM is advantageous for probing molecular environments of fluorophores to inform on fluorophore behavior that cannot be elucidated with intensity measurements alone. Development of FLIM technologies, analysis, and applications will further advance biological research and clinical assessments.

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Parametric and Nonparametric Classification for Minimizing Misclassification Errors

Nagdeote

Chiwande²

2020

Social Networking and Computational Intelligence

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Convolutional neural network based image segmentation: a review

Cited by 18 publications

References 0 publications

Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism

Autofluorescence imaging of 3D tumor-macrophage microscale cultures resolves spatial and temporal dynamics of macrophage metabolism

Fluorescence lifetime imaging microscopy: fundamentals and advances in instrumentation, analysis, and applications

Parametric and Nonparametric Classification for Minimizing Misclassification Errors

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