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.

利用深度学习技术，通过自动多模式图像配准方法，将快照乳房标本图像与对应的组织学图像进行对齐，以提高光学技术验证的准确性。