A DEEP LEARNING PIPELINE FOR SEGMENTATION OF PROTEUS MIRABILIS COLONY PATTERNS
A DEEP LEARNING PIPELINE FOR SEGMENTATION OF PROTEUS MIRABILIS COLONY PATTERNS
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用于分割奇异变形杆菌菌落模式的深度学习流程
DOI：
10.1101/2022.01.17.475672
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Danino, T.
中科院分区：
文献类型：
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作者：
Doshi, A;Shaw, M;Tonea, R;Minyety, R;Moon, S;Laine, A;Guo, G;Danino, T.
微生物的运动机制是关键的毒力因素，使其在感染期间传播和存活。通常对宏观菌落进行定性分析，而标准的定量方法主要局限于人工测量。最近的研究已经将深度学习应用于微观图像中特定微生物物种的分类和分割，但对宏观菌落分析的关注较少。在这里，我们提出了用于分析变形杆菌菌落的计算工具，这种细菌通过周期性的蜂群产生宏观的类似靶心的图案，这一过程与它的毒性有关。我们提出了一种双任务管道来分割(1)宏观群体，包括微弱的外环；(2)内环边界，振荡群体的独特特征。我们的卷积神经网络用于基于补丁的群体分割，U-Net用于基于VGG-11编码器的环边界分割，分别获得了93.28%和83.24%的测试Dice分数。预测的掩模有时会在我们的自动注释算法的基础上得到改进。我们展示了如何将我们的管道应用于典型的群体分析，使群体分析和精确测量比历史上量化的更复杂的模式特征更加容易。我们工作的实施可以在https://github.com/daninolab/proteus-mirabilis上找到。
The motility mechanisms of microorganisms are critical virulence factors, enabling their spread and survival during infection. Motility is frequently characterized by qualitative analysis of macroscopic colonies, yet the standard quantification method has mainly been limited to manual measurement. Recent studies have applied deep learning for classification and segmentation of specific microbial species in microscopic images, but less work has focused on macroscopic colony analysis. Here, we advance computational tools for analyzing colonies of Proteus mirabilis, a bacterium that produces a macroscopic bullseye-like pattern via periodic swarming, a process implicated in its virulence. We present a dual-task pipeline for segmenting (1) the macroscopic colony including faint outer swarm rings, and (2) internal ring boundaries, unique features of oscillatory swarming. Our convolutional neural network for patch-based colony segmentation and U-Net with a VGG-11 encoder for ring boundary segmentation achieved test Dice scores of 93.28% and 83.24%, respectively. The predicted masks at times improved on the ground truths from our automated annotation algorithms. We demonstrate how application of our pipeline to a typical swarming assay enables ease of colony analysis and precise measurements of more complex pattern features than those which have been historically quantified. An implementation of our work can be found on https://github.com/daninolab/proteus-mirabilis.