Major changes (full Methods here): :tada: Inter-subject alignment is now further refined using registration in unfolded space! :rocket: The default subfield parcellation scheme is now based on the maxprob of seven histology samples. Together these changes align subfield boundaries to within approximately 0.5mm! :computer: Can (optionally) specify alternative U-Net tissue segmentation models, including contrast-agnositic synthseg (https://github.com/khanlab/hippunfold/pull/244) Minor chages: :bug: Bug fix for inner/outer surfaces sometimes being pushed outside of hippocampus (https://github.com/khanlab/hippunfold/pull/236) :bug: Curvature is no longer calculated on a smoothed surface, and is instead normalized via a tanh function (https://github.com/khanlab/hippunfold/pull/242) :memo: Improvements to Documentation clarity Notes: The effects of inter-subject alignment are most evident at high resolution, such as 7T, ex-vivo scanning, or histology. However this will still improve alignment of in-vivo scans, leading to sharper group-averaged images. Check out our full evaluation here The new default subfield parcellation scheme includes the previous labelling from 3D BigBrain with some added corrections. Previous labelling schemes can optionally also be applied. The new synthseg models should be considered experimental. These should be applicable to any scanning modality and sometimes to 3D histology. They also provide better segmentation detail, leading to clearer definition of digitations/gyrifications. However, these models have not yet been formally evaluated, and may stil undergo additional training iterations. Check out our associated toolbox for helpful Python/Matlab analysis tools, tutorials, and examples! Contributors @jordandekraker @akhanf @kaitj @Bradley-Karat