Abeyrathna Dilanga, Ashaduzzaman Md, Malshe Milind, Kalimuthu Jawaharraj, Gadhamshetty Venkataramana, Chundi Parvathi, Subramaniam Mahadevan
Department of Computer Science, University of Nebraska, Omaha, NE, United States.
Civil and Environmental Engineering Department, South Dakota School of Mines & Technology, Rapid City, SD, United States.
Front Microbiol. 2022 Dec 1;13:996400. doi: 10.3389/fmicb.2022.996400. eCollection 2022.
Microbially induced corrosion (MIC) of metal surfaces caused by biofilms has wide-ranging consequences. Analysis of biofilm images to understand the distribution of morphological components in images such as microbial cells, MIC byproducts, and metal surfaces non-occluded by cells can provide insights into assessing the performance of coatings and developing new strategies for corrosion prevention. We present an automated approach based on self-supervised deep learning methods to analyze Scanning Electron Microscope (SEM) images and detect cells and MIC byproducts. The proposed approach develops models that can successfully detect cells, MIC byproducts, and non-occluded surface areas in SEM images with a high degree of accuracy using a low volume of data while requiring minimal expert manual effort for annotating images. We develop deep learning network pipelines involving both contrastive (Barlow Twins) and non-contrastive (MoCoV2) self-learning methods and generate models to classify image patches containing three labels-cells, MIC byproducts, and non-occluded surface areas. Our experimental results based on a dataset containing seven grayscale SEM images show that both Barlow Twin and MoCoV2 models outperform the state-of-the-art supervised learning models achieving prediction accuracy increases of approximately 8 and 6%, respectively. The self-supervised pipelines achieved this superior performance by requiring experts to annotate only ~10% of the input data. We also conducted a qualitative assessment of the proposed approach using experts and validated the classification outputs generated by the self-supervised models. This is perhaps the first attempt toward the application of self-supervised learning to classify biofilm image components and our results show that self-supervised learning methods are highly effective for this task while minimizing the expert annotation effort.
生物膜引起的金属表面微生物诱导腐蚀（MIC）具有广泛的影响。分析生物膜图像以了解图像中形态成分的分布，如微生物细胞、MIC副产物以及未被细胞遮挡的金属表面，可为评估涂层性能和制定新的防腐蚀策略提供见解。我们提出一种基于自监督深度学习方法的自动化方法，用于分析扫描电子显微镜（SEM）图像并检测细胞和MIC副产物。所提出的方法开发出的模型能够使用少量数据以高精度成功检测SEM图像中的细胞、MIC副产物和未被遮挡的表面积，同时所需的专家手动标注工作量最小。我们开发了涉及对比（巴洛双胞胎）和非对比（MoCoV2）自学习方法的深度学习网络管道，并生成模型对包含细胞、MIC副产物和未被遮挡表面积这三个标签的图像块进行分类。我们基于包含七幅灰度SEM图像的数据集进行的实验结果表明，巴洛双胞胎模型和MoCoV2模型均优于当前最先进的监督学习模型，预测准确率分别提高了约8%和6%。自监督管道通过仅要求专家标注约10%的输入数据实现了这种卓越性能。我们还使用专家对所提出的方法进行了定性评估，并验证了自监督模型生成的分类输出。这可能是将自监督学习应用于生物膜图像成分分类的首次尝试，我们的结果表明自监督学习方法在这项任务中非常有效，同时将专家标注工作量降至最低。
