Microscopy on icaoberg

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Mon, 11 Nov 2024 00:00:00 +0000

Whole-brain microscopy datasets are enormous — often terabytes per image — and the field has been generating thousands of them. The bottleneck is no longer acquisition; it’s storage, sharing, and making all of that data usable by researchers who weren’t part of the original experiment.

The Brain Image Library (BIL) was built to solve that problem.

What BIL Provides

BIL is a public, persistent repository for brain microscopy data, hosted at the Pittsburgh Supercomputing Center and serving the broader neuroscience community. Rather than requiring researchers to download multi-terabyte datasets before they can work with them, BIL provides integrated analysis and visualization tools that let users explore data in place — directly through the repository.

CellOrganizer: Learning and Using Cell Geometries for Spatial Cell Simulations

Tue, 01 Jan 2019 00:00:00 +0000

Spatial simulations of cell processes require realistic cell geometries — accurate representations of where organelles are, how they’re shaped, and how they vary from cell to cell. Building those geometries by hand doesn’t scale. CellOrganizer was designed to learn them directly from microscope images.

This chapter describes the full workflow: from image preparation through model training, quality assessment, geometry sampling, and integration with biochemical simulation frameworks.

What CellOrganizer Does

CellOrganizer learns generative statistical models of cell spatial organization from fluorescence microscopy images. A trained model captures not just a single representative cell shape, but the full distribution of variation — in overall cell architecture, organelle count, organelle size and shape, and spatial positioning — across a population of imaged cells.

OMERO.searcher

Thu, 28 Jun 2012 00:00:00 +0000

Searching a database of fluorescence microscopy images by typing keywords works well enough when annotations are complete and consistent. In practice, they rarely are. Images get uploaded with minimal metadata, terminology varies across labs, and no text description fully captures what a subcellular pattern actually looks like.

The real question is: given this image, which other images in the database look like it?

That’s the problem we set out to solve.