AIMC Topic: Microscopy

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DeXtrusion: automatic recognition of epithelial cell extrusion through machine learning in vivo.

Development (Cambridge, England)
Accurately counting and localising cellular events from movies is an important bottleneck of high-content tissue/embryo live imaging. Here, we propose a new methodology based on deep learning that allows automatic detection of cellular events and the...

Ultrasound Localization Microscopy Using Deep Neural Network.

IEEE transactions on ultrasonics, ferroelectrics, and frequency control
Noninvasive imaging of microvascular structures in deep tissues provides morphological and functional information for clinical diagnosis and monitoring. Ultrasound localization microscopy (ULM) is an emerging imaging technique that can generate micro...

Leukocyte differentiation in bronchoalveolar lavage fluids using higher harmonic generation microscopy and deep learning.

PloS one
BACKGROUND: In diseases such as interstitial lung diseases (ILDs), patient diagnosis relies on diagnostic analysis of bronchoalveolar lavage fluid (BALF) and biopsies. Immunological BALF analysis includes differentiation of leukocytes by standard cyt...

Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Journal of biophotonics
Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key ...

DeepSea is an efficient deep-learning model for single-cell segmentation and tracking in time-lapse microscopy.

Cell reports methods
Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the single-cell level with high temporal resolution. Successful application of single-cell time-lapse microscopy re...

Triplet-Net Classification of Contiguous Stem Cell Microscopy Images.

IEEE/ACM transactions on computational biology and bioinformatics
Cellular microscopy imaging is a common form of data acquisition for biological experimentation. Observation of gray-level morphological features allows for the inference of useful biological information such as cellular health and growth status. Cel...

PPSW-SHAP: Towards Interpretable Cell Classification Using Tree-Based SHAP Image Decomposition and Restoration for High-Throughput Bright-Field Imaging.

Cells
Advancements in high-throughput microscopy imaging have transformed cell analytics, enabling functionally relevant, rapid, and in-depth bioanalytics with Artificial Intelligence (AI) as a powerful driving force in cell therapy (CT) manufacturing. Hig...

A Compound Loss Function With Shape Aware Weight Map for Microscopy Cell Segmentation.

IEEE transactions on medical imaging
Microscopy cell segmentation is a crucial step in biological image analysis and a challenging task. In recent years, deep learning has been widely used to tackle this task, with promising results. A critical aspect of training complex neural networks...

Artificial Intelligence in Point-of-Care Testing.

Annals of laboratory medicine
With the projected increase in the global population, current healthcare delivery models will face severe challenges. Rural and remote areas, whether in developed or developing countries, are characterized by the same challenges: the unavailability o...

Digital Histopathology by Infrared Spectroscopic Imaging.

Annual review of analytical chemistry (Palo Alto, Calif.)
Infrared (IR) spectroscopic imaging records spatially resolved molecular vibrational spectra, enabling a comprehensive measurement of the chemical makeup and heterogeneity of biological tissues. Combining this novel contrast mechanism in microscopy w...