Population affinity estimation on a Spanish sample: Testing the validity and accuracy of cranium and mandible online software methods

Sieber, Katie S; García-Donas, Julieta G.

doi:10.1016/j.legalmed.2022.102180

Cited by 2 publications

(1 citation statement)

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“…Images are substantially different (e.g., sparse, mostly isolated target structures; largely uniform background), making that system arguably unsuitable for images of human osteons like the ones used in our study. Although further validation is required to ensure reliable outcomes [ 28 , 29 ], automated approaches have shown promising results as a means to enable more sophisticated statistical analysis, data interpretation and decision making [ 30 ].…”

Section: Introductionmentioning

confidence: 99%

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

Littek

McKenna

Chiam

et al. 2023

Biology

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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.

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Section: Introductionmentioning

confidence: 99%