Automatic deformable registration of histological slides to μCT volume data
Autor: | Christos Bikis, Philippe C. Cattin, Bert Müller, Anna Khimchenko, Simone E. Hieber, Natalia Chicherova |
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Rok vydání: | 2018 |
Předmět: |
Histology
Matching (graph theory) business.industry Computer science Micro computed tomography Jaw bone Normalized mutual information 030218 nuclear medicine & medical imaging Pathology and Forensic Medicine 03 medical and health sciences 0302 clinical medicine Position (vector) Fully automatic Computer vision Position error Artificial intelligence business 030217 neurology & neurosurgery Volume (compression) |
Zdroj: | Journal of Microscopy. 271:49-61 |
ISSN: | 0022-2720 |
Popis: | 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. |
Databáze: | OpenAIRE |
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