Automatic deformable registration of histological slides to μCT volume data

Summary Localizing a histological section in the three‐dimensional dataset of a different imaging modality is a challenging 2D‐3D registration problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro computed tomography (μCT) volume. For the majority of the datasets, the result of localization corresponded to the manual results. However, for some datasets, the matching μCT slice was off the ground‐truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study, we introduce two optimization frameworks based on normalized mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81 % of histological sections with a median position error of 8.4 μm in jaw bone datasets, and the deformable approach improved registration by 33 μm with respect to the median distance error for four histological slides in the cerebellum dataset. Lay description Localising a histological section in the three‐dimensional dataset of a different imaging modality is a challenging problem. In the literature, several approaches have been proposed to solve this problem; however, they cannot be considered as fully automatic. Recently, we developed an automatic algorithm that could successfully find the position of a histological section in a micro‐computed tomography volume. For the majority of the datasets, the result of localisation corresponded to the manual results. However, for some datasets the matching tomography slice was off the ground‐truth position. Furthermore, elastic distortions, due to histological preparation, could not be accounted for in this framework. In the current study we introduce two optimisation frameworks based on normalised mutual information, which enabled us to accurately register histology slides to volume data. The rigid approach allocated 81% of histological sections with a median position error of 8.4 µm in jaw bone datasets, and the deformable approach improved registration by 33 µm in the cerebellum dataset.