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<p>Deep learning has been widely used for forensic age estimation based on CT-derived images, and data volume is critical to enhancing the model performance. However, traditional data augmentation such as flipping, rotating, and adjusting contrast is not well-suited for regression tasks like age estimation. This study applied a data augmentation, R-Mixup to DenseNet121 training for age estimation from CT-derived images of the pubic symphysis. A dataset of 1,200 individuals aged 20–80 years was analyzed, with images segmented into five regions of interest (ROIs): pubic symphysis surface (PSS), ischial tuberosity (IT), pubic dorsal aspect (PDA), pubic ventral aspect (PVA) and pubic tubercle (PT). Results show that R-Mixup significantly enhanced model performance, particularly when combined with transfer learning, reducing estimation MAE errors for all ROI images. PVA was the most informative ROI, achieving the lowest MAE of 4.89 years for females and 6.32 years for males. Weighted estimation based on multiple ROIs further improved accuracy, with the best-performing combinations reaching MAE of 4.55 years (females) and 6.00 years (males). This study demonstrates the effectiveness of CNN-based forensic age estimation, highlighting the benefits of multi-ROI analysis and non-conventional data augmentation. Future research will explore co-training techniques to improve model robustness in cases involving incomplete skeletal remains.</p>

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Weighted estimation of multiple regions in CT-derived images of the pubic symphysis for age estimation using a deep learning framework with R-Mixup augmentation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Zhihe Lyu,
  • Mingzhen Yang,
  • Yongjie Cao,
  • Yihua Zhao,
  • Zhihan Zuo,
  • Huaihan Zhang,
  • Weiwei Zhu,
  • Yuchun Fang,
  • Ping Huang,
  • Ji Zhang

摘要

Deep learning has been widely used for forensic age estimation based on CT-derived images, and data volume is critical to enhancing the model performance. However, traditional data augmentation such as flipping, rotating, and adjusting contrast is not well-suited for regression tasks like age estimation. This study applied a data augmentation, R-Mixup to DenseNet121 training for age estimation from CT-derived images of the pubic symphysis. A dataset of 1,200 individuals aged 20–80 years was analyzed, with images segmented into five regions of interest (ROIs): pubic symphysis surface (PSS), ischial tuberosity (IT), pubic dorsal aspect (PDA), pubic ventral aspect (PVA) and pubic tubercle (PT). Results show that R-Mixup significantly enhanced model performance, particularly when combined with transfer learning, reducing estimation MAE errors for all ROI images. PVA was the most informative ROI, achieving the lowest MAE of 4.89 years for females and 6.32 years for males. Weighted estimation based on multiple ROIs further improved accuracy, with the best-performing combinations reaching MAE of 4.55 years (females) and 6.00 years (males). This study demonstrates the effectiveness of CNN-based forensic age estimation, highlighting the benefits of multi-ROI analysis and non-conventional data augmentation. Future research will explore co-training techniques to improve model robustness in cases involving incomplete skeletal remains.