Quantifying the survival patterns of spiral ganglion cells (SGCs), the cell bodies of auditory-nerve fibers, is critical to studies of sensorineural hearing loss, especially in human temporal bones. The classic method of manual counting is tedious, and, although stereology approaches can be faster, they can only be used to estimate total cell numbers per cochlea. Here, a machine-learning algorithm that automatically identifies, counts, and maps the SGCs in digitized images of semi-serial human temporal-bone sections not only speeds the analysis, with no loss of accuracy, but also allows 3D visualization of the SGCs and fine-grained mapping to cochlear frequency. Applying the algorithm to 62 normal-aging human ears shows significantly faster degeneration of SGCs in the basal than the apical half of the cochlea. Comparison to fiber counts in the same ears shows that the fraction of surviving SGCs lacking a peripheral axon steadily increases with age, reaching more than 50% in the apical cochlea and almost 66% in basal regions.