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Automated Quantification of Hematopoietic Cell &#8211; Stromal Cell Interactions in Histological Images of Undecalcified Bone
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使用细胞表面标记器优化密集的细胞的基于深度学习的细分.

Sunwoo Han1, Khamsone Phasouk1, Jia Zhu1

  • 1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, USA.

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|October 16, 2023
PubMed
概括
此摘要是机器生成的。

密集组织中精确的细胞细分对于空间分析至关重要. 深度学习模型,特别是Cellpose,在针对性训练和参数调整后显著提高了性能,超过了最初的基准.

关键词:
在HSV中,它是HSV.细胞细分 细胞细分 细胞细分计算机视觉 计算机视觉深度学习是一种深度学习.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 图像分析 图像分析
  • 免疫光显微镜的使用方法

背景情况:

  • 精确的细胞细分对于空间分子分析至关重要.
  • 在密集的炎症组织中识别和量化细胞是一个重大挑战.

研究的目的:

  • 评估深度学习模型的细胞细分在免疫光图像的人类单纯疹病毒 (HSV) 感染的皮肤组织.
  • 通过模型训练和参数调来提高细胞细分性能.

主要方法:

  • 在免疫光成像上评估了18个深度学习细胞细分模型.
  • 进一步训练了8个模型,使用目标数据集中的10,000多个实例.
  • 性能最好的模型的微调参数.

主要成果:

  • 最好的预训练模型达到0.516的平均平均精度 (mAP).
  • 在训练后,Cellpose细胞模型达到0.694.4的mAP.
  • 参数调整进一步提高了性能,达到0.711.11的mAP.

结论:

  • 模型选择和有针对性的培训可以显著改善细胞细分性能.
  • 优化模型的性能与人类评估具有竞争力.
  • 数据集中的中等信号噪声比可能会限制最终的性能.