Towards a 2D cortical osseous tissue representation and generation at micro scale. A computational model for bone simulations.

Background And Objective: the acquisition of microscopic images of human bones is a complex and expensive process. Moreover, the objective of obtaining a large data bank with microscopic images in order to carry out massive studies or to train automatic generation algorithms is not an option. Consequently, most of the current work focuses on the analysis of small regions captured by a microscope. The aim is the development of a tool to represent bone tissue at microscopic levels which is suitable for performing physical simulations, as well as for the diagnosis of various diseases. This work includes the whole process from the digitization of a human bone to the generation of bone tissue in a determined area of the bone selected through a cutting plane.

Methods: based on the anatomy of the bone structure, the parameters that allow the representation of the bone tissue at mesoscale level have been analyzed. Although the models are randomly generated, they are based on statistical parameters. The model generator is based on the analysis of images of bone tissue and its parameters, performing a representation of each of its relevant structures in a way that fulfils these parameters.

Results: the tool is useful for the virtual generation of bone tissue that satisfies the main characteristics of the cortical bone. The models obtained have been favorably evaluated in two stages. In the first stage, a scientific group has examined a set of images, in which images of the models generated were mixed with images obtained through traditional methods. Then, the physical characteristics of the generated tissue have been compared with the morphology of the bone tissue.

Conclusions: the model generator allows us to perform precise simulations in order to obtain realistic images with physical characteristics in accordance with reality. It is necessary to emphasize that even though the most relevant structures are included, the proposed model generator can be expanded to include new parameters or elements, so that it can be adapted to new needs. It could even break down randomness and parameterize it completely in order to allow the recreation of the tissue conditions of other studies.