  Correlating neuropathology with neuroimaging findings provides a multiscale
view of pathologic changes in the human organ spanning the meso- to
micro-scales, and is an emerging methodology expected to shed light on numerous
disease states. To gain the most information from this multimodal, multiscale
approach, it is desirable to identify precisely where a histologic tissue
section was taken from within the organ in order to correlate with the tissue
features in exactly the same organ region. Histology-to-organ registration
poses an extra challenge, as any given histologic section can capture only a
small portion of a human organ. Making use of the capabilities of
state-of-the-art deep learning models, we unlock the potential to address and
solve such intricate challenges. Therefore, we create the ATOM benchmark
dataset, sourced from diverse institutions, with the primary objective of
transforming this challenge into a machine learning problem and delivering
outstanding outcomes that enlighten the biomedical community. The performance
of our RegisMCAN model demonstrates the potential of deep learning to
accurately predict where a subregion extracted from an organ image was obtained
from within the overall 3D volume. The code and dataset can be found at:
https://github.com/haizailache999/Image-Registration/tree/main
