Deformable multi-modal image registration for the correlation between optical measurements and histology images

TL;DR

This paper presents a deep learning-based automated multi-modal image registration method that accurately aligns optical and histology images, improving correlation and validation in medical imaging.

Contribution

It introduces an unsupervised deep learning approach based on VoxelMorph for multi-modal image registration, outperforming supervised and manual methods.

Findings

Unsupervised model achieves higher Dice scores and mutual information.

Automated registration reduces human error and improves alignment accuracy.

Method enhances validation of optical imaging technologies.

Abstract

The correlation of optical measurements with a correct pathology label is often hampered by imprecise registration caused by deformations in histology images. This study explores an automated multi-modal image registration technique utilizing deep learning principles to align snapshot breast specimen images with corresponding histology images. The input images, acquired through different modalities, present challenges due to variations in intensities and structural visibility, making linear assumptions inappropriate. An unsupervised and supervised learning approach, based on the VoxelMorph model, was explored, making use of a dataset with manually registered images used as ground truth. Evaluation metrics, including Dice scores and mutual information, reveal that the unsupervised model outperforms the supervised (and manual approach) significantly, achieving superior image alignment. This automated registration approach holds promise for improving the validation of optical technologies by minimizing human errors and inconsistencies associated with manual registration.