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 .
To assist in this task, an automatic yet reliable method would be desirable to eliminate human subjectivity . 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,
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.
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