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

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	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

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

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10.1007/s10967-024-09461-2

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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.",

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AU - Babayew, Rami

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AU - Weiss, Aryeh

AU - Gilad, Erez

AU - Levy, Galit Katarivas

AU - Halevy, Itzhak

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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.

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KW - Fission track analysis

KW - Holmeland security

KW - Nuclear forensics

KW - Safeguards investigations

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

Abstract

Keywords

ASJC Scopus subject areas

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