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Microscopy

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AtomVision: A Machine Vision Library for Atomistic Images.

Journal of chemical information and modeling
Computer vision techniques have immense potential for materials design applications. In this work, we introduce an integrated and general-purpose AtomVision library that can be used to generate and curate microscopy image (such as scanning tunneling ...

Optofluidic imaging meets deep learning: from merging to emerging.

Lab on a chip
Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microsco...

Improving deep learning-based segmentation of diatoms in gigapixel-sized virtual slides by object-based tile positioning and object integrity constraint.

PloS one
Diatoms represent one of the morphologically and taxonomically most diverse groups of microscopic eukaryotes. Light microscopy-based taxonomic identification and enumeration of frustules, the silica shells of these microalgae, is broadly used in aqua...

Artificial intelligence-based tools applied to pathological diagnosis of microbiological diseases.

Pathology, research and practice
Infectious diseases still threaten the global community, especially in resource-limited countries. An accurate diagnosis is paramount to proper patient and public health management. Identification of many microbes still relies on manual microscopic e...

A practical guide to deep-learning light-field microscopy for 3D imaging of biological dynamics.

STAR protocols
Here, we present a step-by-step protocol for the implementation of deep-learning-enhanced light-field microscopy enabling 3D imaging of instantaneous biological processes. We first provide the instructions to build a light-field microscope (LFM) capa...

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence.

Journal of visualized experiments : JoVE
The micronucleus (MN) assay is used worldwide by regulatory bodies to evaluate chemicals for genetic toxicity. The assay can be performed in two ways: by scoring MN in once-divided, cytokinesis-blocked binucleated cells or fully divided mononucleated...

Identifying Bacteria Species on Microscopic Polyculture Images Using Deep Learning.

IEEE journal of biomedical and health informatics
Preliminary microbiological diagnosis usually relies on microscopic examination and, due to the routine culture and bacteriological examination, lasts up to 11 days. Hence, many deep learning methods based on microscopic images were recently introduc...

DeepNoise: Signal and Noise Disentanglement Based on Classifying Fluorescent Microscopy Images via Deep Learning.

Genomics, proteomics & bioinformatics
The high-content image-based assay is commonly leveraged for identifying the phenotypic impact of genetic perturbations in biology field. However, a persistent issue remains unsolved during experiments: the interferential technical noises caused by s...

Looking with new eyes: advanced microscopy and artificial intelligence in reproductive medicine.

Journal of assisted reproduction and genetics
Microscopy has long played a pivotal role in the field of assisted reproductive technology (ART). The advent of artificial intelligence (AI) has opened the door for new approaches to sperm and oocyte assessment and selection, with the potential for i...

ReCSAI: recursive compressed sensing artificial intelligence for confocal lifetime localization microscopy.

BMC bioinformatics
BACKGROUND: Localization-based super-resolution microscopy resolves macromolecular structures down to a few nanometers by computationally reconstructing fluorescent emitter coordinates from diffraction-limited spots. The most commonly used algorithms...