AI Medical Compendium Topic

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Time-Lapse Imaging

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3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework.

Applied optics
We present a compact 3D diffractive microscope that can be inserted directly in a cell incubator for long-term observation of developing organisms. Our setup is particularly simple and robust, since it does not include any moving parts and is compati...

Automatic improvement of deep learning-based cell segmentation in time-lapse microscopy by neural architecture search.

Bioinformatics (Oxford, England)
MOTIVATION: Live cell segmentation is a crucial step in biological image analysis and is also a challenging task because time-lapse microscopy cell sequences usually exhibit complex spatial structures and complicated temporal behaviors. In recent yea...

Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.

Nucleic acids research
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biologica...

Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction.

IEEE/ACM transactions on computational biology and bioinformatics
Cells in culture display diverse motility behaviors that may reflect differences in cell state and function, providing motivation to discriminate between different motility behaviors. Current methods to do so rely upon manual feature engineering. How...

Phase imaging with computational specificity (PICS) for measuring dry mass changes in sub-cellular compartments.

Nature communications
Due to its specificity, fluorescence microscopy has become a quintessential imaging tool in cell biology. However, photobleaching, phototoxicity, and related artifacts continue to limit fluorescence microscopy's utility. Recently, it has been shown t...

CytoCensus, mapping cell identity and division in tissues and organs using machine learning.

eLife
A major challenge in cell and developmental biology is the automated identification and quantitation of cells in complex multilayered tissues. We developed CytoCensus: an easily deployed implementation of supervised machine learning that extends conv...

Discovering the hidden messages within cell trajectories using a deep learning approach for in vitro evaluation of cancer drug treatments.

Scientific reports
We describe a novel method to achieve a universal, massive, and fully automated analysis of cell motility behaviours, starting from time-lapse microscopy images. The approach was inspired by the recent successes in application of machine learning for...