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Automatic deformable registration of histological slides to μCT volume data
Author(s) -
Chicherova N.,
Hieber S.E.,
Khimchenko A.,
Bikis C.,
Müller B.,
Cattin P.
Publication year - 2018
Publication title -
journal of microscopy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.569
H-Index - 111
eISSN - 1365-2818
pISSN - 0022-2720
DOI - 10.1111/jmi.12692
Subject(s) - computer science , ground truth , artificial intelligence , image registration , volume (thermodynamics) , position (finance) , mutual information , matching (statistics) , computer vision , pattern recognition (psychology) , computed tomography , modality (human–computer interaction) , image (mathematics) , radiology , medicine , pathology , physics , finance , quantum mechanics , economics
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.