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Updated: Jun 26, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

Published on: April 8, 2015

优化基于深度学习的密集细胞的细分,使用细胞表面标记物.

Sunwoo Han¹, Khamsone Phasouk², Jia Zhu^3,4

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

BMC medical informatics and decision making

|May 15, 2024

概括

此摘要是机器生成的。

在密集组织中精确的细胞细分是具有挑战性的. 像Cellpose这样的微调深度学习模型在空间分子分析中显著提高了细胞识别性能.

关键词:

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

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相关实验视频

Last Updated: Jun 26, 2025

Automated Quantification of Hematopoietic Cell – Stromal Cell Interactions in Histological Images of Undecalcified Bone

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Published on: April 8, 2015

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科学领域:

计算病理学计算病理学
生物图像分析分析
机器学习用于生物成像.

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仍然是一个重大挑战.

研究的目的:

评估复杂组织图像中细胞细分的深度学习模型.
通过模型训练和参数调来提高细胞细分性能.

主要方法:

评估了18种深度学习细胞细分模型,用于在感染人类简单疹病毒 (HSV) 时的皮肤免疫光图像.
进一步训练了8个模型,使用了来自目标图像集的10,000多个实例.
优化了最高性能模型的参数,以提高准确性.

主要成果:

最好的模型在微调之前达到0.516的平均平均精度 (mAP).
在训练后,Cellpose细胞模型的mAP达到0.694.
进一步的参数调整将mAP提高到0.711,超过了最初的基准指标.

结论:

模型选择和有针对性的培训是提高细胞细分性能的关键.
微调的模型表现出与人类专家可比的性能.
图像中的中等信号噪声比影响了最终模型的准确性.