Deformable multi-modal image registration for the correlation between optical measurements and histology images
This work addresses the challenge of correlating optical measurements with pathology labels in medical imaging, which is incremental as it builds on existing VoxelMorph models for a specific domain.
The study tackled the problem of imprecise registration between optical measurements and histology images due to deformations, by developing an automated multi-modal image registration technique using deep learning. The unsupervised model significantly outperformed supervised and manual approaches, achieving superior image alignment as measured by Dice scores and mutual information.
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.