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Abstract <p>To automate the radiation-induced foci (RIF) analysis, we have followed a deep learning approach which consists of two stages; first a pretrained neural network called SAM2 is used to detect the cell nuclei in each fluorescent image, then the trained neural network YOLO on our foci-annotated data is used to detect foci in each nucleus. Based on this model, we have created a web service on the “Writer Framework.” The web service allows the user to observe the identified cell nuclei in an uploaded fluorescent image, choose the desired nuclei, automatically get the marked foci and obtain the numerical characteristics such as the number of RIF per cell.</p>

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Web Service for Automated Detection and Analysis of Radiation-Induced Foci in Cell Nuclei

  • 期刊论文

  • 发表日期：

    2026年7月20日

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  • S. Shadmehri,
  • T. Bezhanyan,
  • M. Yu. Bondarev,
  • O. I. Streltsova,
  • M. I. Zuev,
  • A. V. Boreyko,
  • T. S. Khramko,
  • M. E. Krupnova

摘要

Abstract

To automate the radiation-induced foci (RIF) analysis, we have followed a deep learning approach which consists of two stages; first a pretrained neural network called SAM2 is used to detect the cell nuclei in each fluorescent image, then the trained neural network YOLO on our foci-annotated data is used to detect foci in each nucleus. Based on this model, we have created a web service on the “Writer Framework.” The web service allows the user to observe the identified cell nuclei in an uploaded fluorescent image, choose the desired nuclei, automatically get the marked foci and obtain the numerical characteristics such as the number of RIF per cell.