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Microscopy

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Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images.

SLAS discovery : advancing life sciences R & D
Advances in microscopy have increased output data volumes, and powerful image analysis methods are required to match. In particular, finding and characterizing nuclei from microscopy images, a core cytometry task, remains difficult to automate. While...

Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study.

Histopathology
AIMS: The nuclear proliferation biomarker Ki67 plays potential prognostic and predictive roles in breast cancer treatment. However, the lack of interpathologist consistency in Ki67 assessment limits the clinical use of Ki67. The aim of this article w...

celldeath: A tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition.

PloS one
Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell dea...

Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks.

BMC bioinformatics
BACKGROUND: Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells an...

Artificial Intelligence and Cellular Segmentation in Tissue Microscopy Images.

The American journal of pathology
With applications in object detection, image feature extraction, image classification, and image segmentation, artificial intelligence is facilitating high-throughput analysis of image data in a variety of biomedical imaging disciplines, ranging from...

Adaptive adversarial neural networks for the analysis of lossy and domain-shifted datasets of medical images.

Nature biomedical engineering
In machine learning for image-based medical diagnostics, supervised convolutional neural networks are typically trained with large and expertly annotated datasets obtained using high-resolution imaging systems. Moreover, the network's performance can...

Volumetric monitoring of airborne particulate matter concentration using smartphone-based digital holographic microscopy and deep learning.

Journal of hazardous materials
Airborne particulate matter (PM) has become a global environmental issue. This PM has harmful effects on public health and precision industries. Conventional air-quality monitoring methods usually utilize expensive equipment, and they are cumbersome ...

Recursive Deep Prior Video: A super resolution algorithm for time-lapse microscopy of organ-on-chip experiments.

Medical image analysis
Biological experiments based on organ-on-chips (OOCs) exploit light Time-Lapse Microscopy (TLM) for a direct observation of cell movement that is an observable signature of underlying biological processes. A high spatial resolution is essential to ca...

Dynamic Imaging and Characterization of Volatile Aerosols in E-Cigarette Emissions Using Deep Learning-Based Holographic Microscopy.

ACS sensors
Various volatile aerosols have been associated with adverse health effects; however, characterization of these aerosols is challenging due to their dynamic nature. Here, we present a method that directly measures the volatility of particulate matter ...

DeepSeed Local Graph Matching for Densely Packed Cells Tracking.

IEEE/ACM transactions on computational biology and bioinformatics
The tracking of densely packed plant cells across microscopy image sequences is very challenging, because their appearance change greatly over time. A local graph matching algorithm was proposed to track such cells by exploiting the tight spatial top...