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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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使用深度学习对刺激的拉曼组织学进行注释和自动化单细胞实例细分的协议.

Abhishek Bhattacharya¹, Eric Landgraf², Cheng Jiang²

¹University of Michigan, Ann Arbor, MI 48109, USA; NYU Langone, New York, NY 10016, USA.

|November 29, 2025

概括

此摘要是机器生成的。

本研究介绍了一种使用人工智能在无标签刺激拉曼组织学 (SRH) 图像中进行自动细胞细分的方法. 开发的协议使单细胞空间分析用于神经外科应用.

关键词:

生物信息学是一种生物信息学.生物技术和生物工程生物技术和生物工程计算机科学计算机科学显微镜显微镜是指使用显微镜.一个单细胞的单细胞.

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Last Updated: Jan 10, 2026

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科学领域:

生物医学光学生物医学光学
计算病理学计算病理学
人工智能在医学中的应用

背景情况:

刺激拉曼组织学 (SRH) 提供无标签,高分辨率的分子成像,没有染色.
术内神经外科成像需要快速准确的细胞分析.
自动细分对于复杂组织微环境的定量分析至关重要.

研究的目的:

开发细胞标记和人工智能模型培训的协议,用于在手术内SRH神经外科图像中的自动细胞细分.
为了对SRH数据进行单细胞空间分析.
为SRH图像提供有效注释和分析工具.

主要方法:

与SRH成像相容的细胞标签协议.
在手术内SRH数据上对AI模型进行自动化细胞细分的培训.
使用ELUCIDATE (基于Web的SRH单元格注释工具) 和DetectSRH (Python库) 来进行分析.

主要成果:

在SRH图像上成功实施了细胞标签和人工智能驱动细分协议.
使用开发的工具,证明了单细胞空间分析的可行性.
从神经外科病例中在手术期间获得的SRH图像得到了有效的分析.

结论:

提出的协议和工具促进了SRH神经外科成像中的自动细胞细分和单细胞空间分析.
这种方法增强了无标签光学成像在手术内决策的实用性.
未来的应用可能包括手术期间实时组织表征.