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High-throughput Image Analysis of Tumor Spheroids: A User-friendly Software Application to Measure the Size of Spheroids Automatically and Accurately
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SpheroScan:一个用户友好的深度学习工具,用于球形图像分析.

Akshay Akshay1,2, Mitali Katoch3, Masoud Abedi4

  • 1Functional Urology Research Group, Department for BioMedical Research DBMR, University of Bern, 3008 Bern, Switzerland.

GigaScience
|October 27, 2023
PubMed
概括
此摘要是机器生成的。

我们开发了SpheroScan,这是一种用于分析3D球形图像的自动化工具. 这种基于深度学习的解决方案增强了球状试验的可重现性和吞吐量,推进了3D细胞培养研究.

关键词:
3D球形形状的球形体.图像分析 图像分析面具R-CNN是指一个R-CNN的面具.深度学习是一种深度学习.高通量选的高通量选图像分割 图像细分 图像细分

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科学领域:

  • 生物医学工程 生物医学工程
  • 细胞生物学 细胞生物学
  • 药物发现 药物发现 药物发现

背景情况:

  • 三维 (3D) 球形模型提供了一个比传统的二维文化更具生理相关的微环境.
  • 3D球形测试提高了对细胞行为的理解,药物的有效性和毒性.
  • 缺乏自动化工具阻碍了3D球形图像分析中的可复制性和吞吐量.

研究的目的:

  • 开发一个自动化,用户友好的工具来分析3D球形图像.
  • 为了提高3D球形测试的可复制性和吞吐量.

主要方法:

  • 开发了SpheroScan,这是一款利用深度学习的基于网络的工具 (用卷积神经网络掩盖区域 - R-CNN).
  • 在不同实验条件 (IncuCyte和常规显微镜) 的球形图像上训练了深度学习模型.
  • 采用图像检测和细分用于自动化分析.

主要成果:

  • SpheroScan为大型图像数据集提供自动化分析.
  • 该工具提供交互式可视化功能,用于深入的数据探索.
  • 对验证和测试数据集的性能评估显示出有希望的结果.

结论:

  • SpheroScan显著推进了3D球形图像分析.
  • 该工具有助于在研究中广泛采用3D球形模型.
  • 开源代码和教程可用于SpheroScan.