מרק לסט

אקדמי בכיר

Image segmentation and classification for fission track analysis for nuclear forensics using U-net model

Noam Elgad, Rami Babayew, Mark Last, Aryeh Weiss, Erez Gilad,Galit Katarivas Levy, Itzhak Halevy

This study introduces a novel methodology for the detection and classification of fission track (FT) clusters in microscope images, employing state-of-the-art deep learning techniques for segmentation and classification (Elgad in nuclear forensics—fission track analysis—star segmentation and classification using deep learning, Ben-Gurion University, 2022). The U-Net model, a fully convolutional network, was used to carry out the segmentation of various star-like patterns in both single-class and multi-class scenarios.

שפת פרסום אנגלית
דפים 2321-2337
כתב עת Journal of Radioanalytical and Nuclear Chemistry
כרך 333
נושא מספר 5
סטטוס פרסום פורסם - 01.05.2024

Keywords

Computer vision
Fission track analysis
Holmeland security
Nuclear forensics
Safeguards investigations
U-Net

ASJC Scopus subject areas

Analytical Chemistry
Nuclear Energy and Engineering
Radiology Nuclear Medicine and imaging
Pollution
Spectroscopy
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis
גישה למסמך
10.1007/s10967-024-09461-2
קבצים וקישורים אחרים
Link to publication in Scopus