用于光学测量与组织学图像之间关联的可变形多模态图像配准
Deformable multi-modal image registration for the correlation between optical measurements and histology images
AI总结：
本研究提出基于VoxelMorph的自动化多模态图像配准技术，解决组织学图像变形导致光学测量与病理标签关联受阻的问题，实验证明无监督模型显著优于有监督及手动方法，有望提升光学技术验证的准确性。
摘要：
光学测量与正确病理标签的关联常常因组织学图像变形导致的不精确配准而受阻。本研究探索了一种利用深度学习原理的自动化多模态图像配准技术，将乳腺样本快照图像与相应的组织学图像进行对齐。通过不同模态获取的输入图像，由于强度和结构可见性的变化而带来挑战，使得线性假设不再适用。研究探索了基于VoxelMorph模型的无监督和有监督学习方法，利用包含手动配准图像作为真实参考的数据集。包括Dice分数和互信息在内的评估指标显示，无监督模型显著优于有监督（及手动）方法，实现了更优的图像对齐。这种自动化配准方法有望通过减少与手动配准相关的人为误差和不一致性，改善光学技术的验证。
英文摘要：
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.
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