Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds
Abstract
:1. Introduction
2. Methodology
2.1. Particle Segmentation: Automated Shannon-Entropy-Optimized Slope Difference Distribution (SEO-SDD) Method
- 1.
- Set the initial range of threshold selection by choosing the appropriate upper, B, and lower, A, limits. The range can be read from the curve of the differential of slope difference (shown in Figure 1c). The upper limit B differentiates the background from particles, and the lower limit A differentiates particles with different intensities.
- 2.
- Calculate the median and its corresponding Shannon entropy. The essence of the binary search algorithm lies in iteratively narrowing the search range by comparing the median value, , with the currently considered optimal threshold. As the actual optimal value remains to be identified, we compute the Shannon entropy of the median, , at each iteration and compare it with the entropy from the previous iteration.
- 3.
- Narrow the search range by comparing and . If the entropy obtained using the current threshold is higher than , then adjust the search range to between A and . Otherwise, adjust the search range to between and B.
- 4.
- Reaching the maximum H when . The threshold value that maximizes is the best threshold for nanoparticle segmentation when A is equal to B.
2.2. Unsupervised Machine Learning for Nanoparticle/Aggregate Shape Categorization
2.3. Sample Preparation and TEM Imaging
3. Results and Discussion
3.1. Effectiveness of SEO-SDD Method
3.1.1. Simulated Image
3.1.2. Experimental TEM Images of DND Aggregates
3.2. Particle and Aggregate Shape Categorization through Unsupervised Machine Learning
3.3. Cryo-TEM Image of DND Aggregates in PBS
3.4. DND Uptake in HeLa Cells
4. Conclusions
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Methods | Number of Particles | Accuracy (%) |
---|---|---|
Ground truth | 1028 | 100 |
SEO-SDD | 997 | 96.98 |
Dynamic threshold | 915 | 89.00 |
Histogram threshold | 740 | 71.98 |
Methods | Number | RE (%) |
---|---|---|
Manual counting | 20,362 | 0 |
SEO-SDD | 19,934 | 2.10 |
Dynamic threshold | 20,906 | 2.67 |
Histogram threshold | 12,360 | 39.30 |
Methods | Number | RE (%) |
---|---|---|
Manual counting | 2932 | 0 |
SEO-SDD | 2816 | 3.96 |
Dynamic threshold | 26,170 | 792.56 |
Histogram threshold | 1191 | 59.38 |
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Zhou, L.; Wen, H.; Kuschnerus, I.C.; Chang, S.L.Y. Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds. Nanomaterials 2024, 14, 1169. https://doi.org/10.3390/nano14141169
Zhou L, Wen H, Kuschnerus IC, Chang SLY. Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds. Nanomaterials. 2024; 14(14):1169. https://doi.org/10.3390/nano14141169
Chicago/Turabian StyleZhou, Lishi, Haotian Wen, Inga C. Kuschnerus, and Shery L. Y. Chang. 2024. "Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds" Nanomaterials 14, no. 14: 1169. https://doi.org/10.3390/nano14141169
APA StyleZhou, L., Wen, H., Kuschnerus, I. C., & Chang, S. L. Y. (2024). Efficientand Robust Automated Segmentation of Nanoparticles and Aggregates from Transmission Electron Microscopy Images with Highly Complex Backgrounds. Nanomaterials, 14(14), 1169. https://doi.org/10.3390/nano14141169