Extracellular matrix molecules provide biochemical and topographical cues that influence cell growth in vivo and in vitro. Effects of topographical cues on hippocampal neuron growth were examined after 14 days in vitro. Neurons from hippocampi of rat embryos were grown on poly-L-lysine-coated silicon surfaces containing fields of pillars with varying geometries. Photolithography was used to fabricate 1 microm high pillar arrays with different widths and spacings. Beta(III)-tubulin and MAP-2 immunocytochemistry and scanning electron microscopy were used to describe neuronal processes. Automated two-dimensional tracing software quantified process orientation and length. Process growth on smooth surfaces was random, while growth on pillared surfaces exhibited the most faithful alignment to pillar geometries with smallest gap sizes. Neurite lengths were significantly longer on pillars with the smallest inter-pillar spacings (gaps) and 2 microm pillar widths. These data indicate that physical cues affect neuron growth, suggesting that extracellular matrix topography may contribute to cell growth and differentiation. These results demonstrate new strategies for directing and promoting neuronal growth that will facilitate studies of synapse formation and function and provide methods to establish defined neural networks.

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Background: There is a need for integrative and quantitative methods to investigate the structural and functional relations among elements of complex systems, such as the neurovascular unit (NVU), that involve multiple cell types, microvasculatures, and various genomic/proteomic/ionic functional entities. Methods: Vascular casting and selective labeling enabled simultaneous three-dimensional imaging of the microvasculature, cell nuclei, and cytoplasmic stains. Multidimensional segmentation was achieved by (i) bleed-through removal and attenuation correction; (ii) independent segmentation and morphometry for each corrected channel; and (iii) spatially associative feature computation across channels. The combined measurements enabled cell classification based on nuclear morphometry, cytoplasmic signals, and distance from vascular elements. Specific spatial relations among the NVU elements could be quantified. Results: A software system combining nuclear and vessel segmentation codes and associative features was con-

An automated method is presented for selecting optimal parameter settings for vessel/neurite segmentation algorithms using the minimum description length principle and a recursive random search algorithm. It trades off a probabilistic measure of image-content coverage against its conciseness. It enables nonexpert users to select parameter settings objectively, without knowledge of underlying algorithms, broadening the applicability of the segmentation algorithm, and delivering higher morphometric accuracy. It enables adaptation of parameters across batches of images. It simplifies the user interface to just one optional parameter and reduces the cost of technical support. Finally, the method is modular, extensible, and amenable to parallel computation. The method is applied to 223 images of human retinas and cultured neurons, from four different sources, using a single segmentation algorithm with eight parameters. Improvements in segmentation quality compared to default settings using 1000 iterations ranged from 4.7%-21%. Paired t-tests showed that improvements are statistically significant (p < 0.0005). Most of the improvement occurred in the first 44 iterations. Improvements in description lengths and agreement with the ground truth were strongly correlated (p = 0.78).

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