In the world of computer vision, engineers need to build software that can recognize parts of an image or 3D visual field. Think, for example, a self-driving car that has to recognize the edge of a road or brake lights ahead.
Biologists who want to find their way through the microscopic world inside a cell face a related challenge. To understand different components of the cell — and how they might change under important conditions, like growth or disease — you have to understand precisely where they are and what they look like. And that means knowing the exact location of those components’ edges, down to the pixel.
Enter the Allen Cell Structure Segmenter, a python-based, open-source toolkit incorporating machine learning techniques that does for cell biology what computer vision techniques have done for self-driving cars, broadly speaking. The tool helps scientists navigate complex images of cells by automatically capturing the boundaries of structures inside them.
The toolkit was developed by researchers at the Allen Institute for Cell Science, a division of the Allen Institute, as a way to probe 3D images in the Allen Cell Collection, a catalog of live human stem cell lines gene-edited so that different structures glow with fluorescent colors under the microscope. The collection includes 38 cell lines tagging 34 different cellular structures, with more coming down the pike. The researchers have collected an enormous number of images, and realized they needed an automated and accurate way to navigate the many different structures, whose shape in the fluorescent images varies from tiny dots to smooth boundaries to ruffled edges.
