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AI-assisted image analysis

A collection of 6 posts

Mapping the microbiome: FISH-based approaches for microbial community visualization

AI-assisted image analysis

Mapping the microbiome: FISH-based approaches for microbial community visualization

Understanding what human-associated bacterial biofilms look like has major translational potential and medical impact. Here, we discuss RNA-FISH-based approaches for visualization of microbial communities.

Fluorescent image of biological cells

AI-assisted image analysis

Triple Threat: AI-assisted Multiview Confocal Imaging

One group of researchers is bringing deep learning to that workhorse of bioimaging: confocal microscopy. In a recent paper, Wu et al. present a new microscopy platform that builds on confocal using AI approaches and technical innovations.

From Actin to Z-stacks: The Imaging Journey

AI-assisted image analysis

From Actin to Z-stacks: The Imaging Journey

Bioimaging is a well-established and highly effective tool for interrogating biological systems. In this article, we’ll focus on the process, practice, and problems associated with setting up, running, and analyzing a fluorescence microscopy experiment.

Pancreatic tissue section image

AI-assisted image analysis

Of tumors and TMEs: new findings in pancreatic cancer spatial heterogeneity

Grünwald et al. make major progress towards untangling cancer heterogeneity with a recent paper in Cell.

Putting the Pieces Together: Spatial Transcriptomics Methods and Multi-omic Integration

AI-assisted image analysis

Putting the Pieces Together: Spatial Transcriptomics Methods and Multi-omic Integration

In this article, we'll give an overview of the different spatial sequencing methods currently available, discuss analysis approaches and challenges for spatial data, and cover methods for integrating single cell and spatial transcriptomics.

How to train your model: LIVECell for AI-assisted cell segmentation

cell segmentation

How to train your model: LIVECell for AI-assisted cell segmentation

Edlund et al. created a dataset of phase-contrast images encompassing including 8 morphologically different cell types and trained a CNN-based model to evaluate the model’s cell segmentation accuracy.