arXiv 2302.096292023-02cs.CVcs.LG

BiofilmScanner：一种从生物膜图像中获取细菌细胞形态属性的计算智能方法

BiofilmScanner: A Computational Intelligence Approach to Obtain Bacterial Cell Morphological Attributes from Biofilm Image

GM Core System Tools & Automation（通用汽车核心系统工具与自动化部）；GE HealthCare（通用电气医疗集团）；University of South Carolina（南卡罗来纳大学）；South Dakota Mines（南达科他矿业学院）

Md Hafizur Rahman, Md Ali Azam, Md Abir Hossen, Shankarachary Ragi, Venkataramana Gadhamshetty

备注：: Submitted to Pattern Recognition

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AI总结：

针对现有SRB生物膜细胞形态提取方法效率低、误差高的问题，提出结合不变矩的Yolact深度学习方法BiofilmScanner，在速度和F1-score上均优于此前的Mask-RCNN、DLv3+方法。

摘要：

Desulfovibrio alaskensis G20（DA-G20）被用作硫酸盐还原菌（SRB）的研究模型，这类细菌与微生物引发的腐蚀问题相关。基于SRB的生物膜被认为是每年造成数十亿美元金属基础设施生物腐蚀的原因。了解不同生长阶段SRB生物膜中细菌细胞形状和尺寸属性的提取方法，将有助于防腐技术的设计。然而，当前方法存在诸多问题，包括几何属性提取耗时、效率低且错误率高。本文提出BiofilmScanner，这是一种结合不变矩的、基于Yolact的深度学习方法，用于解决这些问题。我们的方法可高效检测和分割SRB图像中的细菌细胞，同时通过不变矩以低误差测量分割后细胞的几何特征。数值实验表明，在检测、分割和测量细胞几何属性方面，BiofilmScanner的速度是我们此前提出的Mask-RCNN和DLv3+方法的2.1倍和6.8倍。此外，BiofilmScanner的F1-score达到85.28%，而Mask-RCNN和DLv3+的F1-score分别为77.67%和75.18%。

英文摘要：

Desulfovibrio alaskensis G20 (DA-G20) is utilized as a model for sulfate-reducing bacteria (SRB) that are associated with corrosion issues caused by microorganisms. SRB-based biofilms are thought to be responsible for the billion-dollar-per-year bio-corrosion of metal infrastructure. Understanding the extraction of the bacterial cells' shape and size properties in the SRB-biofilm at different growth stages will assist with the design of anti-corrosion techniques. However, numerous issues affect current approaches, including time-consuming geometric property extraction, low efficiency, and high error rates. This paper proposes BiofilScanner, a Yolact-based deep learning method integrated with invariant moments to address these problems. Our approach efficiently detects and segments bacterial cells in an SRB image while simultaneously invariant moments measure the geometric characteristics of the segmented cells with low errors. The numerical experiments of the proposed method demonstrate that the BiofilmScanner is 2.1x and 6.8x faster than our earlier Mask-RCNN and DLv3+ methods for detecting, segmenting, and measuring the geometric properties of the cell. Furthermore, the BiofilmScanner achieved an F1-score of 85.28% while Mask-RCNN and DLv3+ obtained F1-scores of 77.67% and 75.18%, respectively.

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