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Automated contour detection in spine radiographs and computed tomography reconstructions for forensic comparative identification

Abstract : This study was conducted to test an automated method to identify unknown individuals. It relies on a previous radiographic file and uses an edge-based comparison of lumbar CT/PMCT reconstructions and radiographs. The living group was composed of 15 clinical lumbar spine CT scans and 15 paired radiographs belonging to the same patients. The deceased group consisted of 5 lumbar spine PMCT scans and 5 paired antemortem radiographs of deceased individuals plus the 15 unpaired radiographs belonging to the living. An automated method using image filtering (anisotropic diffusion) and edge detection (Canny filter) provided image contours. Cross comparisons of all the exams in each group were performed using similarity measurements under the affine registration hypothesis. The Dice coefficient and Hausdorff distance values were significantly linked (p< 0.001 and p= 0.001 respectively) to the matched examinations in the living group (p < 0.001; pseudo-R2 = 0.70). 12 of the 15 examinations were correctly paired, 2 were wrongly paired and 3 were not paired when they must have been. In the deceased group, the Hausdorff distance was significantly linked (p= 0.018) to the matched examinations (p< 0.001; pseudo-R2 = 0.62; Dice coefficient p= 0.138). The paired examinations were all correctly found, but one was wrongly paired. The negative predictive value was above 98% for both groups. We highlighted the feasibility of comparative radiological identification using automated edge detection in cross-modality (CT/PMCT scan and radiographs) examinations. This method could be of significant help to a radiologist or coroner in identifying unknown cadavers.
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Submitted on : Monday, October 18, 2021 - 10:35:17 PM
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Julien Ognard, Lucile Deloire, Claire Saccardy, Valérie Burdin, Douraied Ben Salem. Automated contour detection in spine radiographs and computed tomography reconstructions for forensic comparative identification. Forensic Science, Medicine, and Pathology, Humana Press, 2020, 16 (1), pp.99-106. ⟨10.1007/s12024-019-00189-0⟩. ⟨hal-03384436⟩



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