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希拉细胞的等离子膜的体积学语义实例细分

Cefa Karabağ¹, Martin L Jones², Constantino Carlos Reyes-Aldasoro¹

¹giCentre, Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, London EC1V 0HB, UK.

Journal of imaging

|July 31, 2024

概括

这项研究提出了一种无监督的方法,用于使用电子显微镜对HeLa细胞等离子膜进行细分. 开发的算法准确地识别和分离细胞,展示了核和细胞细分的高性能指标.

科学领域:

细胞生物学细胞生物学
生物医学成像技术生物医学成像技术
计算生物学计算生物学

背景情况:

细胞结构的准确细分对于理解细胞生物学至关重要.
序列块面扫描电子显微镜 (SBF-SEM) 提供高分辨率的3D超结构数据.
需要自动化细分方法来高效处理大型SBF-SEM数据集.

研究的目的:

为HeLa细胞的等离子膜开发一个无监督的体积语义实例细分算法.
为了从3D SBF-SEM数据中准确识别和分离单个细胞.
为可重复性研究提供开源代码和数据.

主要方法:

在HeLa细胞中无监督的体积语义实例是血膜的细分.
利用树脂背景细分来创建用于细胞识别和排名的距离地图.
通过切片连接细胞中心体,重建3D细胞实例.
切割感兴趣的区域并应用传统的图像处理来进行核细分和细胞分离.

主要成果:

该算法在核和细胞细分方面实现了卓越的准确性 (AC) 和雅卡德相似度指数 (JI).
核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975. 核的细分:JI=0.9665,AC=0.9975
细胞细分 (包括细胞核):JI=0.8711,AC=0.9655. 细胞细分 (包括细胞核):JI=0.8711,AC=0.9655.
细胞细分 (不包括细胞核):JI=0.8094,AC=0.9629. 细胞细分 (不包括细胞核):JI=0.8094,AC=0.9629.
即使有背景限制,也成功分割了核包裹.

结论:

开发的无监督算法提供了从SBF-SEM数据中HeLa细胞的精确体积语义实例细分.
该方法强大且可适应,在各种细胞成像研究中具有潜在的应用.
开放地共享代码和数据促进了透明度,并促进了该领域的进一步进展.

关键词:

这种细胞是HeLa细胞.血膜是一种等离子体膜.语义实例细分语义实例细分

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