TITLE
REVIEW OF FUNCTIONAL THEORIES AND NUMERICAL APPROACHES IN ELASTOHYDRODYNAMIC LUBRICATION OF RADIAL SHAFT SEALS
AUTHOR(S)
Georgi Panchev
ABSTRACT
Accurate segmentation of medical images is essential for diagnosis and treatment planning. Traditional manual methods in platforms such as 3D Slicer are precise but time-consuming and dependent on the operator’s expertise. With the development of deep learning, neural networks (particularly U-Net) have enabled automatic segmentation with improved efficiency and precision. This paper compares manual and automatic segmentation on MRI spleen scans. Manual methods included thresholding, painting, and tracing techniques, while automatic segmentation was performed using the MONAI Label framework integrated with U-Net. Evaluation with the Dice coefficient showed high overlap between methods, with values above 0.9 in most cases. Results confirm that deep learning - based segmentation provides faster and reliable outcomes, supporting its application in clinical practice.
DOI
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How to cite this article:
Georgi Panchev, REVIEW OF FUNCTIONAL THEORIES AND NUMERICAL APPROACHES IN ELASTOHYDRODYNAMIC LUBRICATION OF RADIAL SHAFT SEALS, UNITECH – SELECTED PAPERS - 2025