Automatic deformable registration of histological slides to μCT volume data

Abstract

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.

Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

“…Because of this, we used a mutual information-based registration approach, rather than a landmark based method. Chicherova et al demonstrated successful registration between microCT and histology in 3D using a SURF (Speeded Up Robust Features) algorithm 40 . Since SURF is based on the automatic identification of corresponding landmarks, it appears to work well on histochemical stainings such as the HE staining presented in their publication.…”

“…Therefore, it is reasonable to further evaluate whether the registration quality can be improved by forfeiting the assumption that the microtome cuts strictly on a plane. Additionally, Chicherova et al settled on a deformation model based on Legendre-polynomials in order to avoid the flexibility of the b-splines 40 . This is indeed a reasonable approach when applied in dense tissues like bone and cerebellum.…”

Elastic transformation of histological slices allows precise co-registration with microCT data sets for a refined virtual histology approach

“…Similar approaches for microCT and histology co-registration were presented by others. Chicherova et al demonstrated successful registration between microCT and histology in 3D using a SURF (Speeded Up Robust Features) algorithm 36 . Since SURF is based on the automatic identi cation of corresponding landmarks, it appears to work well on histochemical stainings such as the HE staining presented in their publication.…”

“…Therefore, it is reasonable to further evaluate whether the registration quality can be improved by forfeiting the assumption that the microtome cuts strictly on a plane. Additionally, Chicherova et al settled on a deformation model based on Legendre-polynomials in order to avoid the exibility of the b-splines 36 . This is indeed a reasonable approach when applied in dense tissues like bone and cerebellum.…”

X-ray imaging of human brain tissue down to the molecule level

“…For such a comparison, one needs powerful registration algorithms [48]. N. Chicherova et al performed dedicated research activities for the non-rigid slice-to-volume registration as required for the identification of the counterparts of histology slides within CT-data [49][50][51]. This task is particularly challenging due to the many degrees of freedom [52].…”