Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
NEURAL NETWORKS IN THE ANALYSIS OF 3D MEDICAL MODELS
0
Zitationen
5
Autoren
2025
Jahr
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.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.911 Zit.
nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation
2020 · 8.068 Zit.
Calculation of average PSNR differences between RD-curves
2001 · 4.093 Zit.
Magnetic Resonance Classification of Lumbar Intervertebral Disc Degeneration
2001 · 3.932 Zit.
Vertebral fracture assessment using a semiquantitative technique
1993 · 3.628 Zit.