Image segmentation and classification for fission track analysis for nuclear forensics using U-net model
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 classif...
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Veröffentlicht in: | Journal of radioanalytical and nuclear chemistry 2024-05, Vol.333 (5), p.2321-2337 |
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Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
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Online-Zugang: | Volltext |
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Zusammenfassung: | 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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ISSN: | 0236-5731 1588-2780 |
DOI: | 10.1007/s10967-024-09461-2 |