Nanotechnology has been bringing numerous advances in all its applications fields, ranging from electronics to
medicine. Nanomedicine, as it is called the emerging field of the meeting of pharmaceutical, biomedical sciences
and nanotechnology, investigates the potentials of nanoparticles to improve diagnostics and therapy in healthcare
[1, 2]. Interactions of these particles with the biological environment are dependent on some key factors, as particle
size, shape and distribution. These aspects impact the particles efficacy, safety, and toxicological profiles [1–4].
Therefore, it is important to develop an accurate method to measure particle size, distribution, and characterize them
to assess their quality and safety [2].
To assist in this task, an automatic yet reliable method would be desirable to eliminate human subjectivity [5]. Recently,
deep learning is emerging as a powerful tool and will continue to attract considerable interests in microscopy
image analysis, as object detection and segmentation, extraction of regions of interest (ROIs), image classification,
etc. [6].
In this thesis, we will employ a well-established deep neural network to automatically detect, segment, and classify
nanoparticles in microscopy images. Additionally, we will extend the method to measure the size of our nanoparticles,
which also requires annotation of the particles’ measurements beforehand. Finally, we will evaluate our approach and
analyze our outcomes.
The thesis will include the following points:
• Getting familiar with the nanoparticle characterization problem and tools applied in this work.
• Extend the dataset’s annotations with the nanoparticles measurements.
• Modify the chosen network to predict the nanoparticles’ size.
• Employ the modified network to detect, segment, and classify nanoparticles and predict their size.
• Evaluate the results according to appropriate metrics for the task.
• Elaboration of further improvements for the proposed method.
Academic advisors:
References
[1] D. Bobo, K. J. Robinson, J. Islam, K. J. Thurecht, and S. R. Corrie, “Nanoparticle-based medicines: a review
of fda-approved materials and clinical trials to date,” Pharmaceutical research, vol. 33, no. 10, pp. 2373–2387,
2016.
[2] F. Caputo, J. Clogston, L. Calzolai, M. R¨osslein, and A. Prina-Mello, “Measuring particle size distribution of
nanoparticle enabled medicinal products, the joint view of euncl and nci-ncl. a step by step approach combining
orthogonal measurements with increasing complexity,” Journal of Controlled Release, vol. 299, pp. 31–43, 2019.
[3] V. Mohanraj and Y. Chen, “Nanoparticles-a review,” Tropical journal of pharmaceutical research, vol. 5, no. 1,
pp. 561–573, 2006.
[4] A. G. Roca, L. Guti´errez, H. Gavil´an, M. E. F. Brollo, S. Veintemillas-Verdaguer, and M. del Puerto Morales, “Design
strategies for shape-controlled magnetic iron oxide nanoparticles,” Advanced drug delivery reviews, vol. 138,
pp. 68–104, 2019.
[5] B. Sun and A. S. Barnard, “Texture based image classification for nanoparticle surface characterisation and machine
learning,” Journal of Physics: Materials, vol. 1, no. 1, p. 016001, 2018.
[6] L. Lu, Y. Zheng, G. Carneiro, and L. Yang, “Deep learning and convolutional neural networks for medical image
computing,” Advances in Computer Vision and Pattern Recognition; Springer: New York, NY, USA, 2017.