Automatic Segmentation of Osteonal Microstructure in Human Cortical Bone Using Deep Learning: A Proof of Concept

SIMPLE SUMMARY: Human cortical bone microstructure assessment is used in biological and forensic anthropology for different purposes. For example, studies have investigated the relationship between the microstructural features and age, while others have examined bone microstructure for identification of animal and human bone. The present research is a pilot study investigating the possibility of automatic analysis of human bone microstructure microphotographs through the application of deep learning. The aim of this study is to explore the feasibility of identification of intact and fragmentary osteons in human cortical bone. Our results demonstrate the potential of deep learning for differentiation of osteonal structures, although a larger dataset and further refinement of the model is required in the future to confirm our preliminary results and provide a more accurate identification of osteonal structures. ABSTRACT: Cortical bone microstructure assessment in biological and forensic anthropology can assist with the estimation of age-at-death and animal-human differentiation, for example. Osteonal structures within cortical bone are the key feature under analysis, with osteon frequency and metric parameters providing crucial information for the assessment. Currently, the histomorphological assessment consists of a time-consuming manual process for which specific training is required. Our work investigates the feasibility of automatic analysis of human bone microstructure images through the application of deep learning. In this paper, we use a U-Net architecture to address the semantic segmentation of such images into three classes: intact osteons, fragmentary osteons, and background. Data augmentation was used to avoid overfitting. We evaluated our fully automatic approach using a sample of 99 microphotographs. The contours of intact and fragmentary osteons were traced manually to provide ground truth. The Dice coefficients were 0.73 for intact osteons, 0.38 for fragmented osteons, and 0.81 for background, giving an average of 0.64. The Dice coefficient of the binary classification osteon-background was 0.82. Although further refinement of the initial model and tests with larger datasets are needed, this study provides, to the best of our knowledge, the first proof of concept for the use of computer vision and deep learning for differentiating both intact and fragmentary osteons in human cortical bone. This approach has the potential to widen and facilitate the use of histomorphological assessment in the biological and forensic anthropology communities.