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序列块面扫描电子显微镜的三维重建,使用基于半监督深度学习的语义分割

Dal-Jae Yun^1,2, Junhyeong Park¹, Youngkwon Haam³

¹Emerging Research Instruments Group, Strategic Technology Research Institute, Korea Research Institute of Standards and Science (KRISS), 267 Gajeong-ro, Yuseong-gu, Daejeon 34113, Republic of Korea.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|June 11, 2025

概括

本研究引入了一种半监督式学习方法,以提高连续块面扫描电子显微镜 (SBF-SEM) 中的细分精度,以进行详细的3D器官分析,减少手动注释的需求.

科学领域:

电子显微镜电子显微镜
计算生物学计算生物学
机器学习机器学习

背景情况:

序列块面扫描电子显微镜 (SBF-SEM) 能够对细胞超结构进行高分辨率的3D重建.
准确的SBF-SEM数据细分对于可靠的分析至关重要,但需要大量的劳动力.

研究的目的:

开发一种半监督式学习方法,以便在SBF-SEM中进行高效和准确的细分.
为了减少SBF-SEM数据处理所需的手动注释工作.

主要方法:

使用一种新的细分插值方法来估计细分形状和稀疏注释图像之间的位置.
深度神经网络使用这种半监督方法进行训练.
验证使用F-1分数和K折交叉验证.

主要成果:

在验证过程中,该方法在小鼠大脑细胞中获得了0.89的F-1得分,在倒置图像中获得了0.84的F-1.
在一个独立的数据集上进行测试,得分为0.84 (老鼠大脑细胞) 和0.80 (逆向病例).
使用行进立方体算法的重建使有机体的3D分析成为可能.

结论:

拟议的半监督学习方法显著提高了SBF-SEM细分精度,同时最大限度地减少了手动注释.
这种方法可以为复杂的有机体进行详细的3D超结构分析.
在生物学和医学中存在先进的细胞成像和分析的潜在应用.

关键词:

这是一个3D重建的重建.DNN DNN 在线这就是SBF-SEM.语义细分语义细分语义细分语义细分半监督学习半监督学习

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