Realistic Cranial Defect Design and Integration Approach in CT-Based Segmented Skulls BT Tabanli Kafatasi B l tlemesinde Ger ek i Kraniyal Defekt Tasarimi ve Entegrasyon Yakla simi

ONAT F. E., Armagan Keskin R., Apaydinli B., Gunay A., Atici F. M., Kulle E., ...Daha Fazla

33rd IEEE Conference on Signal Processing and Communications Applications, SIU 2025, İstanbul, Türkiye, 25 - 28 Haziran 2025, (Tam Metin Bildiri) identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/siu66497.2025.11112344
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: Cranial Defect, Deep Learning, Principal Component Analysis
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Machine learning is playing an increasingly important role in completing and analyzing missing or damaged regions in cranial reconstruction. In this paper, we propose an approach that embeds realistically generated synthetic defects into healthy skull samples after reducing the boundary coordinates of manually designed implants to two dimensions via Principal Component Analysis (PCA). Experiments on eleven different implant samples demonstrate that more than 98% of the variance is preserved after PCA, thereby confirming that anatomical consistency is largely maintained. This realistic approach aims to overcome the limited diversity in existing datasets and enhance the performance of machine learning-based methods for defect modeling. Our comprehensive and anatomically realistic synthetic dataset makes a significant contribution to cranial reconstruction processes and provides a solid foundation for deep learning-driven solutions.