Attention U-Net for Precision Skeletal Segmentation in Chest X-Ray Imaging: Advancing Person Identification Techniques in Forensic Science

This study presents an advanced method for post-mortem person identification using the segmentation of skeletal structures from chest X-ray images. The proposed approach employs the Attention U-Net architecture, enhanced with gated attention mechanisms, to refine segmentation by emphasizing spatially relevant anatomical features while suppressing irrelevant details. By isolating skeletal structures which remain stable over time compared to soft tissues, this method leverages bones as reliable biometric markers for identity verification. The model integrates custom-designed encoder and decoder blocks with attention gates, achieving high segmentation precision. To evaluate the impact of architectural choices, we conducted an ablation study comparing Attention U-Net with and without attention mechanisms, alongside an analysis of data augmentation effects. Training and evaluation were performed on a curated chest X-ray dataset, with segmentation performance measured using Dice score, precision, and loss functions, achieving over 98% precision and 94% Dice score. The extracted bone structures were further processed to derive unique biometric patterns, enabling robust and privacy-preserving person identification. Our findings highlight the effectiveness of attention mechanisms in improving segmentation accuracy and underscore the potential of chest bone-based biometrics in forensic and medical imaging. This work paves the way for integrating artificial intelligence into real-world forensic workflows, offering a non-invasive and reliable solution for post-mortem identification.