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基于深度学习的TEM图像分析,用于完全自动检测瘤细胞内部的金纳米粒子

Amrit Kaphle¹, Sandun Jayarathna¹, Hem Moktan¹

¹Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Houston, Texas 77030, USA.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|July 25, 2023

概括

深度学习方法使用传输电子显微镜图像自动检测瘤细胞中的金纳米粒子. 这种方法提供了精确的量化,有助于癌症治疗的发展.

科学领域:

纳米技术纳米技术
生物医学成像技术生物医学成像技术
人工智能的人工智能

背景情况:

传输电子显微镜 (TEM) 对于在瘤细胞内可视化黄金纳米粒子 (GNP) 是至关重要的.
对于GNP的TEM图像的传统分析是手动的,缓慢的,容易出错的.

研究的目的:

开发和验证深度学习 (DL) 模型,用于在蜂TEM图像中自动检测和量化GNP.
提高癌症治疗药物GNP细胞相互作用分析的效率和准确性.

主要方法:

使用转移学习和调整的输入参数实现并优化了"你只看一次 (YOLO) "v5模型.
训练并验证了78个原始和1240个增强的TEM图像的数据集上的瘤细胞的模型.
评估模型性能使用F1分数和平均平均精度 (mAP) 在工会值以上的各种交叉点.

主要成果:

获得了0.982的F1最高分数,表明在GNP检测中具有高精度和回忆.
在0.50.0的IOU值下,获得了0.989的平均精度.
证明了DL模型能够精确估计GNP数量和细胞内的位置.

结论:

开发的基于DL的方法可以完全自动和准确地分析包含GNP的TEM图像.
这种新的工具可以显著推进研究和开发基于黄金纳米粒子的癌症治疗方法.
为纳米医学应用提供了GNP载瘤细胞的精确建模.

关键词:

这是YOLOv5的.细胞图像细胞图像深度学习是一种深度学习.金纳米颗粒的金子纳米颗粒传输电子显微镜的使用

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相关概念视频

Immunogold Electron Microscopy

Immunogold Electron Microscopy

Immunoelectron microscopy utilizes immunogold labeling of endogenous proteins with specific antibodies to detect and localize these proteins in cells and tissues. The procedure provides insights into the distribution and quantification of protein under different stimulation conditions offering clues about their functions. Conjugating highly electron-dense gold particles with primary or secondary antibodies allow antigen detection on and within cells, with high resolution and specificity.