BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.
BCM3D 2.0: accurate segmentation of single bacterial cells in dense biofilms using computationally generated intermediate image representations.
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DOI：
10.1038/s41522-022-00362-4
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发表时间：
2022-12-18
影响因子：
9.2
通讯作者：
Gahlmann, Andreas
中科院分区：
文献类型：
--
作者：
Zhang, Ji;Wang, Yibo;Donarski, Eric D.;Toma, Tanjin T.;Miles, Madeline T.;Acton, Scott T.;Gahlmann, Andreas
在三维（3D）荧光延时图像中准确检测和分割单细胞对于观察称为生物膜的大型细菌群落中的单个细胞行为至关重要。基于机器学习的图像分析的最新进展正在以越来越高的准确性提供这种能力。利用深度卷积神经网络（cnn）的能力，我们最近开发了3D细菌细胞形态测定法（BCM3D），这是一种集成的图像分析管道，将深度学习与传统图像分析相结合，以检测和分割3D荧光图像中的单个生物膜细胞。虽然首次发布的BCM3D （BCM3D 1.0）实现了最先进的3D细菌细胞分割精度，但低信号背景比（sbr）和非常致密的生物膜图像仍然具有挑战性。在这里，我们提出BCM3D 2.0来解决这一挑战。BCM3D 2.0完全补充了BCM3D 1.0中使用的方法。我们没有训练cnn进行体素分类，而是训练cnn将3D荧光图像转换为中间3D图像表示，当适当组合时，这些表示比单个实验图像更适合传统的数学图像处理。使用这种方法，即使对于非常低的sbr和/或高细胞密度的生物膜图像，也可以获得改进的分割结果。改进的细胞分割精度反过来又提高了通过3D空间和时间跟踪单个细胞的精度。这种能力为在细胞水平上研究细菌生物膜中的时间依赖性现象打开了大门。
Accurate detection and segmentation of single cells in three-dimensional (3D) fluorescence time-lapse images is essential for observing individual cell behaviors in large bacterial communities called biofilms. Recent progress in machine-learning-based image analysis is providing this capability with ever-increasing accuracy. Leveraging the capabilities of deep convolutional neural networks (CNNs), we recently developed bacterial cell morphometry in 3D (BCM3D), an integrated image analysis pipeline that combines deep learning with conventional image analysis to detect and segment single biofilm-dwelling cells in 3D fluorescence images. While the first release of BCM3D (BCM3D 1.0) achieved state-of-the-art 3D bacterial cell segmentation accuracies, low signal-to-background ratios (SBRs) and images of very dense biofilms remained challenging. Here, we present BCM3D 2.0 to address this challenge. BCM3D 2.0 is entirely complementary to the approach utilized in BCM3D 1.0. Instead of training CNNs to perform voxel classification, we trained CNNs to translate 3D fluorescence images into intermediate 3D image representations that are, when combined appropriately, more amenable to conventional mathematical image processing than a single experimental image. Using this approach, improved segmentation results are obtained even for very low SBRs and/or high cell density biofilm images. The improved cell segmentation accuracies in turn enable improved accuracies of tracking individual cells through 3D space and time. This capability opens the door to investigating time-dependent phenomena in bacterial biofilms at the cellular level.
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影响因子：
10.6
作者：
Magnusson KE;Jalden J;Gilbert PM;Blau HM
通讯作者：
Blau HM
细胞跟踪精度测量基于无环的图表的比较。
影响因子：
3.7
作者：
Matula P;Maška M;Sorokin DV;Matula P;Ortiz-de-Solórzano C;Kozubek M
通讯作者：
Kozubek M
影响因子：
5.4
作者：
Parthasarathy R
通讯作者：
Parthasarathy R
DOI：
10.1007/s10577-021-09656-3
发表时间：
2021-03
期刊：
Chromosome research : an international journal on the molecular, supramolecular and evolutionary aspects of chromosome biology
影响因子：
--
作者：
Mimori-Kiyosue Y
通讯作者：
Mimori-Kiyosue Y
影响因子：
5.5
作者：
Manley, Harriet R.;Potter, David L.;Lieschke, Graham J.
通讯作者：
Lieschke, Graham J.