TY - JOUR
T1 - Image segmentation and classification for fission track analysis for nuclear forensics using U-net model
AU - Elgad, Noam
AU - Babayew, Rami
AU - Last, Mark
AU - Weiss, Aryeh
AU - Gilad, Erez
AU - Levy, Galit Katarivas
AU - Halevy, Itzhak
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024/5
Y1 - 2024/5
N2 - 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.
AB - 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.
KW - Computer vision
KW - Fission track analysis
KW - Holmeland security
KW - Nuclear forensics
KW - Safeguards investigations
KW - U-Net
UR - http://www.scopus.com/inward/record.url?scp=85189352150&partnerID=8YFLogxK
U2 - 10.1007/s10967-024-09461-2
DO - 10.1007/s10967-024-09461-2
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AN - SCOPUS:85189352150
SN - 0236-5731
VL - 333
SP - 2321
EP - 2337
JO - Journal of Radioanalytical and Nuclear Chemistry
JF - Journal of Radioanalytical and Nuclear Chemistry
IS - 5
ER -
