  Although the latest advances in MRI technology have allowed the acquisition
of higher resolution images, reliable delineation of cytoarchitectural or
subcortical nuclei boundaries is not possible. As a result, histological images
are still required to identify the exact limits of neuroanatomical structures.
However, histological processing is associated with tissue distortion and
fixation artifacts, which prevent a direct comparison between the two
modalities. Our group has previously proposed a histological procedure based on
celloidin embedding that reduces the amount of artifacts and yields high
quality whole brain histological slices. Celloidin embedded tissue,
nevertheless, still bears distortions that must be corrected. We propose a
computational pipeline designed to semi-automatically process the celloidin
embedded histology and register them to their MRI counterparts. In this paper
we report the accuracy of our pipeline in two whole brain volumes from the
Brain Bank of the Brazilian Aging Brain Study Group (BBBABSG). Results were
assessed by comparison of manual segmentations from two experts in both MRIs
and the registered histological volumes. The two whole brain histology/MRI
datasets were successfully registered using minimal user interaction. We also
point to possible improvements based on recent implementations that could be
added to this pipeline, potentially allowing for higher precision and further
performance gains.
