Bact-3D:密集多层三维细菌生物膜的水平集分割方法

Jie Wang, Rituparna Sarkar, A. Aziz, Andrea Vaccari, Andreas Gahlmann, S. Acton
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引用次数: 12

摘要

在显微镜中,新的超分辨率方法正在出现,它产生的三维图像的分辨率比传统光学显微镜提供的分辨率小十倍。这种技术使探索生物组织的结构和功能成为可能,例如细菌生物膜,它们具有神秘的相互联系和组织。不幸的是,图像分析社区中用于执行分割和其他高级分析的标准工具不能应用于naïvely这些数据。本文介绍了Bact-3D,一种用于分割体外培养的多层活细菌超分辨率图像的3D方法。该方法结合了一种新颖的初始化方法,该方法利用细菌细胞的几何形状以及针对生物应用量身定制的迭代局部水平集进化。在将分割作为细胞检测的前导实验中,Bact-3D匹配或改进了现有两种方法的Dice得分和均方误差,同时大大提高了细胞检测精度。除了提供性能上的改进,本报告还描述了成像分辨率和分割质量之间的权衡。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Bact-3D: A level set segmentation approach for dense multi-layered 3D bacterial biofilms
In microscopy, new super-resolution methods are emerging that produce three-dimensional images at resolutions ten times smaller than that provided by traditional light microscopy. Such technology is enabling the exploration of structure and function in living tissues such as bacterial biofilms that have mysterious interconnections and organization. Unfortunately, the standard tools used in the image analysis community to perform segmentation and other higher-level analyses cannot be applied naïvely to these data. This paper presents Bact-3D, a 3D method for segmenting super-resolution images of multi-leveled, living bacteria cultured in vitro. The method incorporates a novel initialization approach that exploits the geometry of the bacterial cells as well an iterative local level set evolution that is tailored to the biological application. In experiments where segmentation is used as precursor to cell detection, the Bact-3D matches or improves upon the Dice score and mean-squared error of two existing methods, while yielding a substantial improvement in cell detection accuracy. In addition to providing improvements in performance over the state-of-the-art, this report also characterizes the tradeoff between imaging resolution and segmentation quality.
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