Recent advances and applications of deep learning methods in materials science
Deep learning (DL) is one of the fastest-growing topics in materials data science, with
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
rapidly emerging applications spanning atomistic, image-based, spectral, and textual data …
Spatial components of molecular tissue biology
Methods for profiling RNA and protein expression in a spatially resolved manner are rapidly
evolving, making it possible to comprehensively characterize cells and tissues in health and …
evolving, making it possible to comprehensively characterize cells and tissues in health and …
TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines
TrackMate is an automated tracking software used to analyze bioimages and is distributed
as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to …
as a Fiji plugin. Here, we introduce a new version of TrackMate. TrackMate 7 is built to …
Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
A principal challenge in the analysis of tissue imaging data is cell segmentation—the task of
identifying the precise boundary of every cell in an image. To address this problem we …
identifying the precise boundary of every cell in an image. To address this problem we …
Light sheet fluorescence microscopy
Light sheet fluorescence microscopy (LSFM) uses a thin sheet of light to excite only
fluorophores within the focal volume. Light sheet microscopes (LSMs) have a true optical …
fluorophores within the focal volume. Light sheet microscopes (LSMs) have a true optical …
The cellular response to extracellular vesicles is dependent on their cell source and dose
Extracellular vesicles (EVs) have been established to play important roles in cell-cell
communication and shown promise as therapeutic agents. However, we still lack a basic …
communication and shown promise as therapeutic agents. However, we still lack a basic …
Deep learning in image-based phenotypic drug discovery
Modern drug discovery approaches often use high-content imaging to systematically study
the effect on cells of large libraries of chemical compounds. By automatically screening …
the effect on cells of large libraries of chemical compounds. By automatically screening …
Volume electron microscopy
Life exists in three dimensions, but until the turn of the century most electron microscopy
methods provided only 2D image data. Recently, electron microscopy techniques capable of …
methods provided only 2D image data. Recently, electron microscopy techniques capable of …
Deep learning enables fast and dense single-molecule localization with high accuracy
Single-molecule localization microscopy (SMLM) has had remarkable success in imaging
cellular structures with nanometer resolution, but standard analysis algorithms require …
cellular structures with nanometer resolution, but standard analysis algorithms require …
Avoiding a replication crisis in deep-learning-based bioimage analysis
Deep learning algorithms are powerful tools for analyzing, restoring and transforming
bioimaging data. One promise of deep learning is parameter-free one-click image analysis …
bioimaging data. One promise of deep learning is parameter-free one-click image analysis …