Jumutc, V.; Suponenkovs, A.; Bondarenko, A.; Bļizņuks, D.; Lihachev, A. Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation. Sensors2023, 23, 8337.
Jumutc, V.; Suponenkovs, A.; Bondarenko, A.; Bļizņuks, D.; Lihachev, A. Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation. Sensors 2023, 23, 8337.
Jumutc, V.; Suponenkovs, A.; Bondarenko, A.; Bļizņuks, D.; Lihachev, A. Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation. Sensors2023, 23, 8337.
Jumutc, V.; Suponenkovs, A.; Bondarenko, A.; Bļizņuks, D.; Lihachev, A. Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation. Sensors 2023, 23, 8337.
Abstract
The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.
Keywords
Colony-Forming Unit; Deep Learning; Segmentation; U-Net; Encoder-Decoder; Loss Function; Localization
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
