Deformable multi-modal image registration for the correlation between
  optical measurements and histology images
Lianne Feenstra Maud Lambregts Theo J. M Ruers Behdad Dashtbozorg
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.