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Updated: Jan 14, 2026

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

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Published on: April 8, 2015

ReVGG-R2Net:针对微观血细胞细分的优化循环框架.

Mst Shapna Akter¹, Md Fahim Sultan¹, Tasmin Karim¹

¹Department of Computer Science and Engineering, Oakland University, Rochester, MI 48309, USA.

|October 19, 2025

概括

一个新的模型,ReVGG-R2Net,精确地细分各种微观血细胞,即使拥挤. 这种血细胞细分的进步使用了新的架构和新的数据集RaabinWBCSeg,以改善生物医学分析.

科学领域:

医疗成像医学成像
计算生物学计算生物学
生物医学工程生物医学工程

背景情况:

准确的微观血细胞细分对于生物医学分析和诊断至关重要.
现有的模型难以处理多种细胞类型和密集的细胞,限制了细节的捕捉.

研究的目的:

推出ReVGG-R2Net,这是一种用于精确血细胞细分的新型模型.
为了解决当前细分模型中数据集多样性和细胞细节捕获的局限性.
介绍RaabinWBCSeg,用于血液细胞细分任务的综合数据集.

主要方法:

开发了ReVGG-R2Net,将循环块集成到编码器/解码器中,以提高功能精细化.
使用了修改后的VGG16骨干,在编码器中具有反复的功能.
采用基于R2U-Net的解码器,具有反复的特征融合,以提高密集区域的精度.
引入了RaabinWBCSeg数据集,其中包含各种未感染和感染的细胞类型.

主要成果:

ReVGG-R2Net在五个基准数据集 (包括RaabinWBCSeg和BBBC041Seg) 中展示了最先进的 (SOTA) 性能,其中包括RaabinWBCSeg和BBBC041Seg.
该模型有效地捕捉了复杂的细胞结构,并提高了密集的细胞区域的细分精度.

关键词:

生物医学分析分析深度学习是一种深度学习.白血球是白血球的细胞.微观的血细胞微观的血细胞分段化分段化分段化分段化在VGG16中,VGG16是VGG16中的一个.

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拉宾WBCSeg数据集增强了血液细胞细分任务的模型概括性.

结论:

在血液细胞图像细分方面,ReVGG-R2Net提供了显著的进步.
拟议的模型和数据集有助于更准确和更普遍的生物医学分析.
这项工作为通过增强的显微镜图像分析改善诊断铺平了道路.