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：阿拉斯加脱硫弧菌G20（DA-G20）被用作硫酸盐还原菌（SRB）的模型，这类细菌与微生物引起的腐蚀问题相关。基于SRB的生物膜被认为每年导致数十亿美元金属基础设施的生物腐蚀。了解不同生长阶段SRB生物膜中细菌细胞形状和大小属性的提取，将有助于设计防腐技术。然而，当前方法存在诸多问题，包括几何属性提取耗时、效率低下和错误率高。本文提出BiofilmScanner，一种基于Yolact并融合不变矩的深度学习方法来解决这些问题。该方法能高效检测和分割SRB图像中的细菌细胞，同时利用不变矩以低误差测量分割细胞的几何特征。数值实验表明，与早期的Mask-RCNN和DLv3+方法相比，BiofilmScanner在检测、分割和测量细胞几何属性方面的速度分别提升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%。