•A vast literature of subcortical studies is analysed from multiple axes: imaging protocols; regions of interest; methodology.•High-resolution atlases together with machine learning present a great opportunity for subcortical exploration with application to many clinical and research areas.•Detailed atlases target multiple typically ignored – small nuclei; machine learning is able to model the complex relationship between appearance and anatomy with fast inference.•Computational requirements, robustness and validation are associated challenges that we need to address for a widespread use of machine-learning in subcortical segmentation. This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.