The osteocytes are the most abundant and longest living bone cells, embedded in the bone matrix. They are interconnected with each other through dendrites, located in slender canals called canaliculi. The osteocyte lacunae, cavities in which the cells are located, together with the canaliculi form a communication network throughout the bone matrix, permitting transport of nutrients, waste and signals. These cells were firstly considered passive, but lately it has become increasingly clear their role as mechanosensory cells and orchestrators of bone remodeling. Despite recent advances in imaging techniques, none of the available methods can provide an adequate 3D assessment of the lacuno-canalicular network (LCN). The aims of this thesis were to achieve 3D imaging of the LCN with synchrotron radiation X-ray computed tomography (SR-CT) and to develop tools for 3D detection and segmentation of this cell network, leading towards automatic quantification of this structure. We demonstrate the feasibility of parallel beam SR-CT to image in 3D the LCN (voxel~300 nm). This technique can provide data on both the morphology of the cell network and the composition of the bone matrix. Compared to the other 3D imaging methods, this enables imaging of tissue covering a number of cell lacunae three orders of magnitude greater, in a simpler and faster way. This makes possible the study of sets of specimens in order to reach biomedical conclusions. Furthermore, we propose the use of divergent holotomography, to image the ultrastructure of bone tissue (voxel~60 nm). The image reconstruction provides phase maps, obtained after the application of a suitable phase retrieval algorithm. This technique permits assessment of the cell network with higher accuracy and it enables the 3D organization of collagen fibres organization in the bone matrix, to be visualized for the first time. In order to obtain quantitative parameters on the geometry of the cell network, this has to be segmented. Due to the limitations in spatial resolution, canaliculi appear as 3D tube-like structures measuring only 1-3 voxels in diameter. This, combined with the noise, the low contrast and the large size of each image (8 GB), makes the segmentation a difficult task. We propose an image enhancement method, based on a 3D line filter combined with bilateral filtering. This enables improvement in canaliculi detection, reduction of the background noise and cell lacunae preservation. For the image segmentation we developed a method based on variational region growing. We propose two expressions for energy functionals to minimize in order to detect the desired structure, based on the 3D line filter map and the original image. Preliminary quantitative results on human femoral samples are obtained based on connected components analysis and a few observations related to the bone cell network and its relation with the bone matrix are presented.
Imaging the bone cell network with nanoscale synchrotron computed tomography