  Maps of brain microarchitecture are important for understanding neurological
function and behavior, including alterations caused by chronic conditions such
as neurodegenerative disease. Techniques such as knife-edge scanning microscopy
(KESM) provide the potential for whole organ imaging at sub-cellular
resolution. However, multi-terabyte data sizes make manual annotation
impractical and automatic segmentation challenging. Densely packed cells
combined with interconnected microvascular networks are a challenge for current
segmentation algorithms. The massive size of high-throughput microscopy data
necessitates fast and largely unsupervised algorithms. In this paper, we
investigate a fully-convolutional, deep, and densely-connected encoder-decoder
for pixel-wise semantic segmentation. The excessive memory complexity often
encountered with deep and dense networks is mitigated using skip connections,
resulting in fewer parameters and enabling a significant performance increase
over prior architectures. The proposed network provides superior performance
for semantic segmentation problems applied to open-source benchmarks. We
finally demonstrate our network for cellular and microvascular segmentation,
enabling quantitative metrics for organ-scale neurovascular analysis.
