Extracting 3-Dimensional Brain Model From 2-Dimensional Brain CT Images With Deep Learning Derin grenme ile 2-Boyutlu Beyin BT Goruntulerinden 3-Boyutlu Beyin Modelinin Elde Edilmesi

Sune E., Tok R., ESMER G. B.

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/siu66497.2025.11111853
  • Basıldığı Şehir: İstanbul
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: 3D brain modeling, computed tomography (CT), deep learning, image processing
  • Yıldız Teknik Üniversitesi Adresli: Evet

Özet

Clinical imaging techniques such as computerized tomography (CT) are widely used by today's doctors to obtain information about the patient and decide on the appropriate treatment. In addition to these 2D CT images of the patient's relevant organ, a high resolution 3D model can be used to facilitate the preparation process of specialist doctors before risky surgeries and to minimize the problems they may encounter during the operation. Thus, the surgical preparation process can be carried out more efficiently with the model to be calculated. Segmentation has an important place in obtaining a 3D brain model from 2D brain CT images, and in this study, U-Net architecture was used in medical image segmentation. After segmentation, a high- accuracy and easy-to-analyze 3D brain model was calculated with Marching Cubes, a 3D modeling algorithm.