  We present a scalable method for brain cell identification in multiview
confocal light sheet microscopy images. Our algorithmic pipeline includes a
hierarchical registration approach and a novel multiview version of semantic
deconvolution that simultaneously enhance visibility of fluorescent cell
bodies, equalize their contrast, and fuses adjacent views into a single 3D
images on which cell identification is performed with mean shift.
  We present empirical results on a whole-brain image of an adult Arc-dVenus
mouse acquired at 4micron resolution. Based on an annotated test volume
containing 3278 cells, our algorithm achieves an $F_1$ measure of 0.89.
