AIMC Topic: Microscopy, Phase-Contrast

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AI-based Apoptosis Cell Classification Using Phase-contrast Images of K562 Cells.

Anticancer research Mar 1, 2024
BACKGROUND/AIM: This study aimed to automate the classification of cells, particularly in identifying apoptosis, using artificial intelligence (AI) in conjunction with phase-contrast microscopy. The objective was to reduce reliance on manual observat...
Humans Artificial Intelligence Apoptosis Caspases K562 Cells Microscopy, Phase-Contrast
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Untrained deep learning-based differential phase-contrast microscopy.

Optics letters Jul 1, 2023
Quantitative differential phase-contrast (DPC) microscopy produces phase images of transparent objects based on a number of intensity images. To reconstruct the phase, in DPC microscopy, a linearized model for weakly scattering objects is considered;...
Neural Networks, Computer Algorithms Deep Learning Microscopy, Phase-Contrast
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High space-bandwidth in quantitative phase imaging using partially spatially coherent digital holographic microscopy and a deep neural network.

Optics express Nov 23, 2020
Quantitative phase microscopy (QPM) is a label-free technique that enables monitoring of morphological changes at the subcellular level. The performance of the QPM system in terms of spatial sensitivity and resolution depends on the coherence propert...
Humans Neural Networks, Computer Macrophages Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Erythrocytes Holography Microscopy, Phase-Contrast
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GPU-based deep convolutional neural network for tomographic phase microscopy with ℓ1 fitting and regularization.

Journal of biomedical optics Jun 1, 2018
Tomographic phase microscopy (TPM) is a unique imaging modality to measure the three-dimensional refractive index distribution of transparent and semitransparent samples. However, the requirement of the dense sampling in a large range of incident ang...
Humans Neural Networks, Computer Algorithms Imaging, Three-Dimensional Image Processing, Computer-Assisted Cell Count HeLa Cells Signal-To-Noise Ratio Microscopy, Phase-Contrast Tomography
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A multi-stage random forest classifier for phase contrast cell segmentation.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jan 1, 2015
We present a machine learning based approach to automatically detect and segment cells in phase contrast images. The proposed method consists of a multi-stage classification scheme based on random forest (RF) classifier. Both low level and mid level ...
Humans Machine Learning Algorithms Cell Line, Tumor Image Processing, Computer-Assisted Cluster Analysis Artifacts Signal-To-Noise Ratio Microscopy, Phase-Contrast
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