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.

翻译：光学测量与正确病理标签的相关性常因组织学图像中的变形导致配准不精确而受阻。本研究探索了一种基于深度学习原理的全自动多模态图像配准技术，用于将快照乳腺标本图像与对应的组织学图像对齐。由于输入图像通过不同模态获取，其强度差异及结构可见性变化带来挑战，使得线性假设不再适用。本研究基于VoxelMorph模型探索了无监督与有监督学习方法，并使用包含手动配准图像作为金标准的数据集。评估指标（包括Dice评分和互信息）表明，无监督模型在图像对齐效果上显著优于有监督方法（及手动方法），实现了优越的图像配准。这种自动化配准方法通过减少手动配准带来的人为误差和不一致性，有望改善光学技术的验证效果。