Human brain atlases have been evolving tremendously, propelled recently by brain big projects, and driven by sophisticated imaging techniques, advanced brain mapping methods, vast data, analytical strategies, and powerful computing. We overview here this evolution in four categories: content, applications, functionality, and availability, in contrast to other works limited mostly to content. Four atlas generations are distinguished: early cortical maps, print stereotactic atlases, early digital atlases, and advanced brain atlas platforms, and 5 avenues in electronic atlases spanning the last two generations. Content-wise, new electronic atlases are categorized into eight groups considering their scope, parcellation, modality, plurality, scale, ethnicity, abnormality, and a mixture of them. Atlas content developments in these groups are heading in 23 various directions. Application-wise, we overview atlases in neuroeducation, research, and clinics, including stereotactic and functional neurosurgery, neuroradiology, neurology, and stroke. Functionality-wise, tools and functionalities are addressed for atlas creation, navigation, individualization, enabling operations, and application-specific. Availability is discussed in media and platforms, ranging from mobile solutions to leading-edge supercomputers, with three accessibility levels. The major application-wise shift has been from research to clinical practice, particularly in stereotactic and functional neurosurgery, although clinical applications are still lagging behind the atlas content progress. Atlas functionality also has been relatively neglected until recently, as the management of brain data explosion requires powerful tools. We suggest that the future human brain atlas-related research and development activities shall be founded on and benefit from a standard framework containing the core virtual brain model cum the brain atlas platform general architecture.

Objectives Sensitivity of noncontrast computed tomography (NCCT) in detecting hyperacute (<8 hours) and acute (<24 hours) cerebral infarction is low. We propose an automatic method to detect and localize ischemic infarct and to assess its volume from a single NCCT scan. Materials and Methods The method automatically determines attenuation value ranges of cerebrospinal fluid and white and gray matter, separates the brain scan into the left and right hemispheres, and by analyzing hemisphere attenuation value distributions using percentile difference ratios, it detects, localizes, and quantifies the infarct without its segmentation. The method performance was evaluated on 576 patients with clinically confirmed stroke through NCCT scans acquired at 4 centers to measure how it matched with that of experts in detection, localization, and assessment of infarct volume. The time from the onset of symptoms ranged from 1.5 to 72 hours for 450 scans and more than 72 hours for 82 scans, most with pathologic findings in addition to cerebral infarction; the time was unavailable for 44 scans. In addition, the method was compared with the novice’s (with 52 scans) and experienced readers’ infarct detection (with 21 × 2 scans) in early ischemia detection (with the time from the onset of symptoms ranging from 1.5 to 7 hours). Results The method matches 100% the expert’s infarct detection when chronic infarcts, leukoaraiosis cases, and infarct volumes less than 2 cm³ (determined by detection accuracy simulation) are excluded from the analysis. For all cases excluding infarct volumes less than 2 cm³, the method detection accuracy is 95.7%. Overall, the method detection accuracy is 83.2%. The early method detection accuracy (≤3 hours) is 78.4%. The novice detection accuracy is 27.8% (≤3 hours), 37.5% (3 < to ≤ 8 hours), and 77.8% (>8 hours), whereas the expert detection accuracy for these cases is 100%. Moreover, the method detected all 21 early infarcts, of which 15 were missed by the stroke experts and 14 of 15 were missed by a general radiologist. The method performs automatic analysis in approximately 7 seconds. Conclusions The results demonstrate potential benefits of our method for enhancing expert’s performance because it quickly localizes the infarct and detects cases missed by experts, and it is to be considered as an aid in the emergency department because it substantially outperforms novice readers (100% vs 27%) in infarct detection on NCCT.