TIBBIY 2D TASVIRLAR ASOSIDA 3D TASVIRLARNI SHAKLLANTIRISH USULLARI
Kalit so‘zlar:
2D tibbiy tasvirlar, 3D rekonstruksiya, segmentatsiya algoritmlari, U-Net neyron tarmog‘i, Marching Cubes, DICOM format.Annotatsiya
Mazkur ishda 2D tibbiy tasvirlar asosida buyrak organini uch o‘lchamli (3D)
modelini tiklash jarayoni yoritilgan bo‘lib, unda tibbiy tasvirlarni segmentatsiyalash, 3D
rekonstruksiya va vizualizatsiya bosqichlari batafsil tahlil qilingan. Ayniqsa, chuqur o‘qitish
usullariga asoslangan U-Net modelining buyrak segmentatsiyasidagi yuqori aniqligi va
samaradorligi e’tirof etilgan. Shuningdek, 3D sirt hosil qilishda Marching Cubes algoritmini ustun
jihatlari ko‘rsatib berilgan.
Manbalar
Gonzalez R.C., Woods R.E. Digital Image Processing. – 4th ed. – Pearson, 2018.
Ronneberger O., Fischer P., Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation // Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015). – 2015. – P. 234–241. – DOI: 10.1007/978-3-319-24574-4_28.
Lorensen W.E., Cline H.E. Marching Cubes: A High Resolution 3D Surface Construction Algorithm // ACM SIGGRAPH Computer Graphics. – 1987. – Vol. 21, No. 4. – P. 163–169. – DOI: 10.1145/37402.37422.
Fedorov A., Beichel R., Kalpathy-Cramer J. et al. 3D Slicer as an Image Computing Platform for the Quantitative Imaging Network // Magnetic Resonance Imaging. – 2012. – Vol. 30, No. 9. – P. 1323–1341. – DOI: 10.1016/j.mri.2012.05.001.
Zhao Y., Xie L., Chen X. 3D Reconstruction from 2D Medical Images: A Review // Journal of Biomedical Imaging. – 2016. – Article ID 9272856. – DOI: 10.1155/2016/9272856.