Automated landmark identification for human cortical surface-based registration

Volume-based registration (VBR) is the predominant method used in human neuroimaging to compensate for individual variability. However, surface-based registration (SBR) techniques have an inherent advantage over VBR because they respect the topology of the convoluted cortical sheet. There is evidence that existing SBR methods indeed confer a registration advantage over affine VBR. Landmark-SBR constrains registration using explicit landmarks to represent corresponding geographical locations on individual and atlas surfaces. The need for manual landmark identification has been an impediment to the widespread adoption of Landmark-SBR. To circumvent this obstacle, we have implemented and evaluated an automated landmark identification (ALI) algorithm for registration to the human PALS-B12 atlas. We compared ALI performance with that from two trained human raters and one expert anatomical rater (ENR). We employed both quantitative and qualitative quality assurance metrics, including a biologically meaningful analysis of hemispheric asymmetry. ALI performed well across all quality assurance tests, indicating that it yields robust and largely accurate results that require only modest manual correction (< 10 min per subject). ALI largely circumvents human error and bias and enables high throughput analysis of large neuroimaging datasets for inter-subject registration to an atlas.

► We evaluate automated landmark identification (ALI) for surface-based registration. ► We compare ALI reliability along a number of quantitative metrics to human raters. ► Results suggest ALI is rapid and reliable as compared to human raters. ► ALI largely circumvents human error and bias. ► ALI enables high throughput analysis of large neuroimaging datasets.