DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced foci,Computational and Structural Biotechnology Journal

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DeepFoci: Deep learning-based algorithm for fast automatic analysis of DNA double-strand break ionizing radiation-induced foci
Computational and Structural Biotechnology Journal ( IF 4.4 ) Pub Date : 2021-11-18 , DOI: 10.1016/j.csbj.2021.11.019
Tomas Vicar _{1,

2,

3} , Jaromir Gumulec ₃ , Radim Kolar ₁ , Olga Kopecna ₂ , Eva Pagacova ₂ , Iva Falkova ₂ , Martin Falk ₂

Affiliation

DNA double-strand breaks (DSBs), marked by ionizing radiation-induced (repair) foci (IRIFs), are the most serious DNA lesions and are dangerous to human health. IRIF quantification based on confocal microscopy represents the most sensitive and gold-standard method in radiation biodosimetry and allows research on DSB induction and repair at the molecular and single-cell levels. In this study, we introduce DeepFoci – a deep learning-based fully automatic method for IRIF counting and morphometric analysis. DeepFoci is designed to work with 3D multichannel data (trained for 53BP1 and γH2AX) and uses U-Net for nucleus segmentation and IRIF detection, together with maximally stable extremal region-based IRIF segmentation.

中文翻译：

DeepFoci：基于深度学习的算法，用于快速自动分析 DNA 双链断裂电离辐射引起的焦点

以电离辐射诱导（修复）灶（IRIF）为标志的 DNA 双链断裂（DSB）是最严重的 DNA 损伤，对人类健康有害。基于共焦显微镜的 IRIF 定量代表了辐射生物剂量测定中最灵敏和金标准的方法，并允许在分子和单细胞水平上研究 DSB 诱导和修复。在本研究中，我们介绍了 DeepFoci——一种基于深度学习的全自动 IRIF 计数和形态分析方法。 DeepFoci 设计用于处理 3D 多通道数据（针对 53BP1 和 γH2AX 进行训练），并使用 U-Net 进行核分割和 IRIF 检测，以及基于最稳定极值区域的 IRIF 分割。

更新日期：2021-11-18

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