TIBBIY 2D TASVIRLAR ASOSIDA 3D TASVIRLARNI SHAKLLANTIRISH USULLARI
Keywords:
2D tibbiy tasvirlar, 3D rekonstruksiya, segmentatsiya algoritmlari, U-Net neyron tarmog‘i, Marching Cubes, DICOM format.Abstract
This work describes the process of reconstructing a three-dimensional (3D)
model of the kidney organ based on 2D medical images, in which the stages of segmentation, 3D
reconstruction and visualization of medical images are analyzed in detail. In particular, the high
accuracy and efficiency of the U-Net model based on deep learning methods in kidney
segmentation are recognized. Also, the advantages of the Marching Cubes algorithm in generating
3D surfaces are shown.
References
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.