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.

翻译：Desulfovibrio alaskensis G20（DA-G20）被用作硫酸盐还原菌（SRB）的模式菌株，该类细菌与微生物引起的腐蚀问题密切相关。基于SRB的生物膜被认为每年导致数十亿美元的金属基础设施生物腐蚀。理解不同生长阶段SRB生物膜中细菌细胞形状与尺寸特征的提取，将有助于设计抗腐蚀技术。然而，当前方法存在诸多问题，包括费时的几何属性提取、低效率和高错误率。本文提出BiofilmScanner——一种基于Yolact并结合不变矩的深度学习方法来解决这些问题。该方法能高效检测并分割SRB图像中的细菌细胞，同时利用不变矩低误差地测量分割后细胞的几何特征。数值实验表明，BiofilmScanner在细胞检测、分割及几何属性测量方面，速度分别是此前Mask-RCNN和DLv3+方法的2.1倍和6.8倍。此外，BiofilmScanner的F1分数达到85.28%，而Mask-RCNN和DLv3+的F1分数分别为77.67%和75.18%。