Recent advances in computer vision and machine learning underpin a collection of algorithms with an impressive ability to decipher the content of images. These deep learning algorithms are being applied to biological images and are transforming the analysis and interpretation of imaging data. These advances are positioned to render difficult analyses routine and to enable researchers to carry out new, previously impossible experiments. Here we review the intersection between deep learning and cellular image analysis and provide an overview of both the mathematical mechanics and the programming frameworks of deep learning that are pertinent to life scientists. We survey the field's progress in four key applications: image classification, image segmentation, object tracking, and augmented microscopy. Last, we relay our labs' experience with three key aspects of implementing deep learning in the laboratory: annotating training data, selecting and training a range of neural network architectures, and deploying solutions. We also highlight existing datasets and implementations for each surveyed application.
A machine learning approach to the analysis of time-frequency maps, and its application to neural dynamics
Improving the capacity of complex-valued neural networks with a modified gradient descent learning rule
Particle filtering for multiple object tracking in dynamic fluorescence microscopy images: application to microtubule growth analysis
The 2011 Dietary Reference Intakes for Calcium and Vitamin D: what dietetics practitioners need to know
Oufti: an integrated software package for high-accuracy, high-throughput quantitative microscopy analysis
Deep Learning Automates the Quantitative Analysis of Individual Cells in Live-Cell Imaging Experiments
Rapid, precise quantification of bacterial cellular dimensions across a genomic-scale knockout library
Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning
seqFISH Accurately Detects Transcripts in Single Cells and Reveals Robust Spatial Organization in the Hippocampus
Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance
Encoding Growth Factor Identity in the Temporal Dynamics of FOXO3 under the Combinatorial Control of ERK and AKT Kinases
Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays
Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes
Deep learning is combined with massive-scale citizen science to improve large-scale image classification
Convolutional neural networks automate detection for tracking of submicron-scale particles in 2D and 3D
A Structured Tumor-Immune Microenvironment in Triple Negative Breast Cancer Revealed by Multiplexed Ion Beam Imaging
Classification and mutation prediction from non-small cell lung cancer histopathology images using deep learning
An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis
DeepTetrad: high-throughput image analysis of meiotic tetrads by deep learning in Arabidopsis thaliana
Comparison of Cell and Organoid-Level Analysis of Patient-Derived 3D Organoids to Evaluate Tumor Cell Growth Dynamics and Drug Response
Deep learning approach for quantification of organelles and misfolded polypeptide delivery within degradative compartments.
Pulses and delays, anticipation and memory: seeing bacterial stress responses from a single-cell perspective.
Parasitologist-level classification of apicomplexan parasites and host cell with deep cycle transfer learning (DCTL).
A primer on resolving the nanoscale structure of the plasma membrane with light and electron microscopy
Super-resolution recurrent convolutional neural networks for learning with multi-resolution whole slide images
Deep learning in single-molecule microscopy: fundamentals, caveats, and recent developments [Invited
High-Throughput Phenotyping (HTP) Data Reveal Dosage Effect at Growth Stages in Arabidopsis thaliana Irradiated by Gamma Rays
Caenorhabditis elegans Gastrulation: A Model for Understanding How Cells Polarize, Change Shape, and Journey Toward the Center of an Embryo.
Uncovering the prognostic gene signatures for the improvement of risk stratification in cancers by using deep learning algorithm coupled with wavelet transform
Correction of refractive index mismatch-induced aberrations under radially polarized illumination by deep learning
Application and Comparison of Supervised Learning Strategies to Classify Polarity of Epithelial Cell Spheroids in 3D Culture
Developing Electron Microscopy Tools for Profiling Plasma Lipoproteins Using Methyl Cellulose Embedment, Machine Learning and Immunodetection of Apolipoprotein B and Apolipoprotein(a).
MolAICal: a soft tool for 3D drug design of protein targets by artificial intelligence and classical algorithm.
Artificial intelligence-assisted endoscopy changes the definition of mucosal healing in ulcerative colitis
Integration of geoscience frameworks into digital pathology analysis permits quantification of microarchitectural relationships in histological landscapes.
Real-time, wide-field and high-quality single snapshot imaging of optical properties with profile correction using deep learning
Establishment of a morphological atlas of the Caenorhabditis elegans embryo using deep-learning-based 4D segmentation
AggreCount: An unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially-defined manner.
Artificial Intelligence Will Not Replace Health Professionals, but the Proper Use of Artificial Intelligence Will Make Health Professionals Better
A representation learning approach for recovering scatter-corrected spectra from Fourier-transform infrared spectra of tissue samples.
Quantifying drug-induced structural toxicity in hepatocytes and cardiomyocytes derived from hiPSCs using a deep learning method
A fluorescent reporter system enables spatiotemporal analysis of host cell modification during herpes simplex virus-1 replication.
CellTracker: An Automated Toolbox for Single-Cell Segmentation and Tracking of Time-lapse Microscopy Images
Deep-Learning Based Label-Free Classification of Activated and Inactivated Neutrophils for Rapid Immune State Monitoring.
Rapid Computer-Aided Diagnosis of Stroke by Serum Metabolic Fingerprint Based Multi-Modal Recognition
A large-scale optical microscopy image dataset of potato tuber for deep learning based plant cell assessment
Automatic discrimination of human hematopoietic tumor cell lines using a combination of imaging flow cytometry and convolutional neural network.
Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning.
DeepMIB: User-friendly and open-source software for training of deep learning network for biological image segmentation.
Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.
Whole-organ analysis of TGF-β-mediated remodelling of the tumour microenvironment by tissue clearing.
3D microscopy and deep learning reveal the heterogeneity of crown-like structure microenvironments in intact adipose tissue.
AggreCount: an unbiased image analysis tool for identifying and quantifying cellular aggregates in a spatially defined manner.
CellMAPtracer: A User-Friendly Tracking Tool for Long-Term Migratory and Proliferating Cells Associated with FUCCI Systems.
High content, quantitative AFM analysis of the scalable biomechanical properties of extracellular vesicles.
Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.
3DeeCellTracker, a deep learning-based pipeline for segmenting and tracking cells in 3D time lapse images.
Performance of deep learning restoration methods for the extraction of particle dynamics in noisy microscopy image sequences.
Light scattering pattern specific convolutional network static cytometry for label-free classification of cervical cells.
Deep learning-based automated and universal bubble detection and mask extraction in complex two-phase flows.
Quantification of Dendritic Spines Remodeling under Physiological Stimuli and in Pathological Conditions.
In Vitro Measurements of Shear-Mediated Platelet Adhesion Kinematics as Analyzed through Machine Learning.
Deep neural net tracking of human pluripotent stem cells reveals intrinsic behaviors directing morphogenesis.
Texture analysis based on U-Net neural network for intracranial hemorrhage identification predicts early enlargement.
Cells/colony motion of oral keratinocytes determined by non-invasive and quantitative measurement using optical flow predicts epithelial regenerative capacity.
Study on the identification and evaluation of growth years for Paris polyphylla var. yunnanensis using deep learning combined with 2DCOS.
Coronaviruses encompass a large family of viruses that cause the common cold as well as more serious diseases, such as the ongoing outbreak of coronavirus disease 2019 (COVID-19; formally known as 2019-nCoV). Coronaviruses can spread from animals to humans; symptoms include fever, cough, shortness of breath, and breathing difficulties; in more severe cases, infection can lead to death. This feed covers recent research on COVID-19.
STING Receptor Agonists
Stimulator of IFN genes (STING) are a group of transmembrane proteins that are involved in the induction of type I interferon that is important in the innate immune response. The stimulation of STING has been an active area of research in the treatment of cancer and infectious diseases. Here is the latest research on STING receptor agonists.
Chronic Fatigue Syndrome
Chronic fatigue syndrome is a disease characterized by unexplained disabling fatigue; the pathology of which is incompletely understood. Discover the latest research on chronic fatigue syndrome here.
Hereditary Sensory Autonomic Neuropathy
Hereditary Sensory Autonomic Neuropathies are a group of inherited neurodegenerative disorders characterized clinically by loss of sensation and autonomic dysfunction. Here is the latest research on these neuropathies.
Spatio-Temporal Regulation of DNA Repair
DNA repair is a complex process regulated by several different classes of enzymes, including ligases, endonucleases, and polymerases. This feed focuses on the spatial and temporal regulation that accompanies DNA damage signaling and repair enzymes and processes.
Glut1 deficiency, an autosomal dominant, genetic metabolic disorder associated with a deficiency of GLUT1, the protein that transports glucose across the blood brain barrier, is characterized by mental and motor developmental delays and infantile seizures. Follow the latest research on Glut1 deficiency with this feed.
Separation anxiety is a type of anxiety disorder that involves excessive distress and anxiety with separation. This may include separation from places or people to which they have a strong emotional connection with. It often affects children more than adults. Here is the latest research on separation anxiety.
KIF1A Associated Neurological Disorder
KIF1A associated neurological disorder (KAND) is a rare neurodegenerative condition caused by mutations in the KIF1A gene. KAND may present with a wide range and severity of symptoms including stiff or weak leg muscles, low muscle tone, a lack of muscle coordination and balance, and intellectual disability. Find the latest research on KAND here.
Regulation of Vocal-Motor Plasticity
Dopaminergic projections to the basal ganglia and nucleus accumbens shape the learning and plasticity of motivated behaviors across species including the regulation of vocal-motor plasticity and performance in songbirds. Discover the latest research on the regulation of vocal-motor plasticity here.