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

doi:10.1007/s10967-024-09461-2

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

Ben-Gurion University of the Negev, Bar-Ilan University

Research output: Contribution to journal › Article › peer-review

Abstract

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.

Original language	American English
Pages (from-to)	2321-2337
Number of pages	17
Journal	Journal of Radioanalytical and Nuclear Chemistry
Volume	333
Issue number	5
DOIs	https://doi.org/10.1007/s10967-024-09461-2
State	Published - 1 May 2024

Keywords

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

All Science Journal Classification (ASJC) codes

Analytical Chemistry
Nuclear Energy and Engineering
Radiology Nuclear Medicine and imaging
Pollution
Spectroscopy
Public Health, Environmental and Occupational Health
Health, Toxicology and Mutagenesis

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/s10967-024-09461-2

https://link.springer.com/content/pdf/10.1007/s10967-024-09461-2.pdfLicense: CC BY

Cite this

@article{f98a570b933c48b4bae1be498bf39dc9,

title = "Image segmentation and classification for fission track analysis for nuclear forensics using U-net model",

abstract = "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.",

keywords = "Computer vision, Fission track analysis, Holmeland security, Nuclear forensics, Safeguards investigations, U-Net",

author = "Noam Elgad and Rami Babayew and Mark Last and Aryeh Weiss and Erez Gilad and Levy, \{Galit Katarivas\} and Itzhak Halevy",

note = "Publisher Copyright: {\textcopyright} The Author(s) 2024.",

year = "2024",

month = may,

day = "1",

doi = "10.1007/s10967-024-09461-2",

language = "American English",

volume = "333",

pages = "2321--2337",

journal = "Journal of Radioanalytical and Nuclear Chemistry",

issn = "0236-5731",

publisher = "Akademiai Kiado ZRt.",

number = "5",

}

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 - 2024/5/1

Y1 - 2024/5/1

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

M3 - Article

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 -

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

Abstract

Keywords

All Science Journal Classification (ASJC) codes

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this