Deep semantic segmentation of natural and medical images: a review
The semantic image segmentation task consists of classifying each pixel of an image into an
instance, where each instance corresponds to a class. This task is a part of the concept of …
instance, where each instance corresponds to a class. This task is a part of the concept of …
Deep learning for cellular image analysis
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 with an impressive ability to decipher the content of images. These deep learning …
Whole-cell segmentation of tissue images with human-level performance using large-scale data annotation and deep learning
NF Greenwald, G Miller, E Moen, A Kong, A Kagel… - Nature …, 2022 - nature.com
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 …
[HTML][HTML] Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation
Advances in microscopy hold great promise for allowing quantitative and precise
measurement of morphological and molecular phenomena at the single-cell level in …
measurement of morphological and molecular phenomena at the single-cell level in …
[HTML][HTML] LIVECell—A large-scale dataset for label-free live cell segmentation
Light microscopy combined with well-established protocols of two-dimensional cell culture
facilitates high-throughput quantitative imaging to study biological phenomena. Accurate …
facilitates high-throughput quantitative imaging to study biological phenomena. Accurate …
AD-linked R47H-TREM2 mutation induces disease-enhancing microglial states via AKT hyperactivation
The hemizygous R47H variant of triggering receptor expressed on myeloid cells 2 (TREM2),
a microglia-specific gene in the brain, increases risk for late-onset Alzheimer's disease (AD) …
a microglia-specific gene in the brain, increases risk for late-onset Alzheimer's disease (AD) …
DeepImageJ: A user-friendly environment to run deep learning models in ImageJ
E Gómez-de-Mariscal, C García-López-de-Haro… - Nature …, 2021 - nature.com
DeepImageJ is a user-friendly solution that enables the generic use of pre-trained deep
learning models for biomedical image analysis in ImageJ. The deepImageJ environment …
learning models for biomedical image analysis in ImageJ. The deepImageJ environment …
Bringing TrackMate into the era of machine-learning and deep-learning
TrackMate is an automated tracking software used to analyze bioimages and distributed as
a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve …
a Fiji plugin. Here we introduce a new version of TrackMate rewritten to improve …
[HTML][HTML] Deep learning predicts boiling heat transfer
Boiling is arguably Nature's most effective thermal management mechanism that cools
submersed matter through bubble-induced advective transport. Central to the boiling …
submersed matter through bubble-induced advective transport. Central to the boiling …
[HTML][HTML] Automated deep lineage tree analysis using a Bayesian single cell tracking approach
Single-cell methods are beginning to reveal the intrinsic heterogeneity in cell populations,
arising from the interplay of deterministic and stochastic processes. However, it remains …
arising from the interplay of deterministic and stochastic processes. However, it remains …