Mapping the structural and synaptic organization of the central nervous system is fundamental to a principled understanding of neural circuit development and function and an important goal of modern neuroscience. A plethora of new imaging technologies and computational advances have made whole-brain mapping studies more widely accessible. One such volumetric imaging method is known as serial two-photon tomography (STPT). STPT is an automated block-face imaging method in which a brain or other whole-organ specimen is repetitively imaged using multiphoton illumination and physically sectioned using an integrated vibratome. The resultant tile images are stitched in two dimensions to form mosaic whole-section images, and the mosaic images need only be stacked in three dimensions to generate a whole-brain volumetric image. Automated image analysis pipelines may then be employed to mine quantitative information at the whole-brain scale across large cohorts of experimental animals. Here, we describe our methods optimized in the University of Texas Southwestern Whole Brain Microscopy Facility for STPT using the TissueCyte1000 platform and a custom pipeline for whole-brain image analysis including registration into the Allen Institute Common Coordinate Framework version 3.0 (CCF 3.0). Included is a description of the inclusion of supervised machine learning using a voxel-wise random forest model for classification of features of interest, including cell bodies and subcellular structures. The rapidly advancing pace of STPT and other complementary methods for whole-brain mapping and systematic analysis has the potential to generate transformative insights into brain circuitry in both health and disease.