From Tissue Plane to Organ World: A Benchmark Dataset for Multimodal Biomedical Image Registration using Deep Co-Attention Networks

TL;DR

This paper introduces the ATOM benchmark dataset and a deep learning model, RegisMCAN, for precise histology-to-organ registration, enabling better correlation of pathology with neuroimaging across multiscale biomedical images.

Contribution

The paper presents a new benchmark dataset and a deep co-attention network model for accurate multimodal organ registration, addressing a key challenge in biomedical imaging.

Findings

RegisMCAN achieves high accuracy in localizing histologic tissue within 3D organ volumes.

The ATOM dataset facilitates training and benchmarking of registration models across diverse institutions.

Deep learning significantly improves histology-to-organ registration performance.

Abstract

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