Functional subspecialization of human amygdala has been revealed in a variety of studies based on histological, in-vivo imaging, and meta-data. However, most of the existing studies identified functional subregions of amygdala at a group level. In this study, we investigated individualized functional neuroanatomy of amygdala based on 7T resting-state fMRI data with high spatiotemporal resolution. Our results have demonstrated that an improved semi-supervised clustering algorithm successfully parcellated individual subjects’ amygdala into 3 subregions, each of them having distinctive functional connectivity patterns. The individualized functional subregions of amygdala may better capture individual variability in functional neuroanatomy than their group level counterparts.