3D motif detector paper published!

Our latest publication, ‚ÄčRobust and automated detection of subcellular morphological motifs in 3D microscopy images by Meghan K. Driscoll, Erik S. Welf, Andrew R. Jamieson, Kevin M. Dean, Tadamoto Isogai, Reto Fiolka & Gaudenz Danuser, has been published in Nature Methods. Here, we describe a computational workflow for investigating the coupling between 3D cell morphology and intracellular signaling. In particular, we introduce a generic morphological motif detector that uses machine learning to find morphological structures, such as lamillipodia, blebs, and filopodia, given user provided examples of these structures. Although this workflow can be used on images from a wide variety of microscopes, it was especially designed to be used on images from high-resolution light-sheet microscopes.

There is also a Behind the Paper blog post.