U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting
Abstract
:1. Introduction
2. Materials and Methods
2.1. Bacterial Culture
2.2. Assembly of the Imaging Device
2.3. Raw Image Acquisition
2.4. Image Preprocessing
2.5. Colony Identification and Counting
2.5.1. Culture Dish Edge Segmentation
Dataset Preparation and Network Construction
Network Training
2.5.2. Colony Region Separation
Dataset Preparation
Network Training
2.5.3. Colony Counting
Dataset Preparation
Network Architecture
Network Training
2.6. Training Environment
3. Results
3.1. The Imaging System Captures High-Quality Colony Images
3.2. Image Preprocessing
3.3. Petri Dish Edge Segmentation
3.4. Colony Region Separation
3.5. Training and Validation Performance of ResNet50 on Colony Counting
3.6. Test Performance of the Pipeline on Colony Counting of Entire Images
4. Discussion
4.1. U2-Net Is More Suitable for Locating the Edges of Petri Dishes and Extracting Bacterial Colony Areas Compared to Threshold Segmentation
4.2. The ResNet50 Model in Our Proposed Method Functions as an Interchangeable Module
4.3. Our Method Surpasses YOLO, the Segment Anything Model, and OpenCFU in Performance
4.4. The Impact of Bacterial Colony Quantity and Size on the Performance of Our Method
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Predict Class | Ground Truth | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | |
0 | 485 | 36 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 |
1 | 19 | 3002 | 10 | 3 | 1 | 0 | 1 | 0 | 0 | 0 |
2 | 0 | 37 | 458 | 15 | 0 | 0 | 0 | 0 | 0 | 0 |
3 | 0 | 1 | 4 | 97 | 0 | 0 | 0 | 0 | 0 | 0 |
4 | 0 | 0 | 0 | 0 | 58 | 6 | 0 | 0 | 0 | 0 |
5 | 0 | 0 | 0 | 1 | 3 | 56 | 5 | 0 | 0 | 0 |
6 | 0 | 0 | 0 | 0 | 0 | 0 | 71 | 2 | 1 | 0 |
7 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 103 | 1 | 0 |
8 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 61 | 3 |
9 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 75 |
Recall | 96.23% | 97.59% | 97.03% | 83.62% | 89.23% | 90.32% | 87.65% | 98.10% | 96.83% | 96.15% |
Precision | 92.56% | 98.88% | 89.80% | 95.10% | 90.63% | 86.15% | 95.95% | 95.37% | 95.31% | 99.99% |
Recovery | 96.23% | 99.90% | 99.36% | 94.54% | 95.38% | 98.06% | 98.77% | 99.73% | 99.40% | 99.57% |
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Cao, L.; Zeng, L.; Wang, Y.; Cao, J.; Han, Z.; Chen, Y.; Wang, Y.; Zhong, G.; Qiao, S. U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting. Microorganisms 2024, 12, 201. https://doi.org/10.3390/microorganisms12010201
Cao L, Zeng L, Wang Y, Cao J, Han Z, Chen Y, Wang Y, Zhong G, Qiao S. U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting. Microorganisms. 2024; 12(1):201. https://doi.org/10.3390/microorganisms12010201
Chicago/Turabian StyleCao, Libo, Liping Zeng, Yaoxuan Wang, Jiayi Cao, Ziyu Han, Yang Chen, Yuxi Wang, Guowei Zhong, and Shanlei Qiao. 2024. "U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting" Microorganisms 12, no. 1: 201. https://doi.org/10.3390/microorganisms12010201
APA StyleCao, L., Zeng, L., Wang, Y., Cao, J., Han, Z., Chen, Y., Wang, Y., Zhong, G., & Qiao, S. (2024). U2-Net and ResNet50-Based Automatic Pipeline for Bacterial Colony Counting. Microorganisms, 12(1), 201. https://doi.org/10.3390/microorganisms12010201