Fully automated radiologic identification focusing on the sternal bone

A crucial task in forensic investigations is the identification of unknown deceased. In general, secure identification methods rely on a comparison of ante mortem (AM) with post mortem (PM) data. However, available morphologic approaches are often dependent on the expertise and experience of the examiner, and often lack standardisation and statistical evidence. The objective of this study was therefore to overcome the current challenges via developing a fully automated radiologic identification (autoRADid) method based on the sternal bone. An anonymised AM data set consisting of 91 chest computed tomography (CT) scans, as well as an anonymised PM data set of 42 chest CT scans were included in this work. Out of the 91 available AM CT data sets, 42 AM scans corresponded to the 42 PM CT scans. For the fully automated identification analysis, a custom-made python pipeline was developed, which automatically registers AM data to the PM data in question using a two-step registration method. To evaluate the registration procedure and subsequent identification success, the image similarity measures Jaccard Coefficient, Dice Coefficient, and Mutual Information were computed. The highest value for each metric was retrieved in order to analyse the correspondence between AM and PM data. For all three similarity measures, 38 out of the 42 cases were matched correctly. This corresponds to an accuracy of 91.2%. The four unsuccessful cases incorporated surgical interventions taking place between the AM and the PM CT acquisition or poor CT scan quality preventing robust registration results. To conclude, the presented autoRADid method seems to be a promising fully automated tool for a reliable and facile identification of unknown deceased. A final pipeline combining all three similarity measures is open source and publicly available for efficient future identifications of unknown deceased.