The analysis of the three-dimensional arrangement of cells and tissues is fundamental for the understanding of their individual contribution to the whole organism. High-resolution microscopy outputs seriated two-dimensional images in which, due to the experimental limitations in the labeling, the objects of interest are hardly segmentable by automated means. On the other hand, the outcome of manual segmentation is a set of two-dimensional profiles (i.e. closed 2D curves) which cannot be trivially assembled into a three-dimensional skin. Here we report on a python module for Blender which takes as input a set of registered 2D profiles and outputs a 3D mesh. The program interprets curves (bézier curves specifically) as strings of vectors and aligns them in a process akin to gap-enabled, nucleic acid sequence alignment. The alignment results in optimal faces and enables the intercalation of morphed profiles which ensure a smooth transition between consecutive but highly divergent profiles. We describe the process of manually segmenting interesting structures in the public domain program ImageJ and its plugins, importing the closed curves to Blender and the algorithmic details in generating the mesh. The output mesh illustrates the segmented fruit fly and Macrostomum brains in 3D, and allows for its inspection and measurement of surfaces. In addition, we introduce the uses of simple 3D tubes and spheres for the modelling of neuronal structures and their visualization in Blender.

URL:

www.ini.unizh.ch/public/person.php

www.ini.unizh.ch/~acardona/trakem2.html

www.mcdb.ucla.edu/research/hartenstein/

faculty.neuroscience.ucla.edu/institution/personnel