Abstract
Efficient tools allowing the extraction of 2D surfaces from 3D-microscopy data are essential for studies aiming to decipher the complex cellular choreography through which epithelium morphogenesis takes place during development. Most existing methods allow for the extraction of a single smooth manifold of sufficiently high signal intensity and contrast. These methods usually fail when the surface of interest has a rough topography or when its localization is hampered by other surrounding structures of potentially higher contrast. Most importantly, available methods so far do not address the need to extract several surfaces of interest within the same volume. Multiple surface segmentation in most cases entails laborious manual annotations of the various surfaces separately. As automating this task is critical in studies involving tissue-tissue or tissue-matrix interaction, we developed Zellige, a novel software tool allowing the automated extraction of a non-prescribed number of surfaces of varying inclination, contrast, and texture from a 3D image. The tool requires the adjustment of a small set of control parameters, for which we provide an intuitive interface implemented as a Fiji plugin. As a proof of principle of the versatility of Zellige, we demonstrate its performance and robustness on synthetic images and on four different types of biological samples, covering a wide range of biological contexts.
Competing Interest Statement
The authors have declared no competing interest.
