Multi-modal, mobile microscopy for visualization of biological agents
Bibliographic record
Abstract
Biological and biomedical research is often contingent upon microscopy techniques for observation and studying of biological features and processes, and subsequent analysis.For many applications, it is necessary that the selected imaging system provide high spatial resolution and large field-of-view, in order to be able to visualize individual biological structures or agents within the sample, while capturing an area large enough, where meaningful analysis, such as particle tracking, could be performed within a single frame.Various lens-based and lens-free imaging platforms, each with their own sets of advantages and disadvantages, offer different imaging modalities suitable for different specimens and applications, but they all suffer from a main limitation: the trade-off between spatial resolution and field-of-view.This competition cannot be eliminated but could be optimized, based on the chosen imaging system specifications.This work addresses the restrictive trade-off, and introduces a mobile phone-based illumination-imaging platform that maximizes the attainable field-of-view at high resolution, and expands the use of phone screen illumination to a lens-free platform.The thesis transitions from a broad introduction to microscopy in the biological and biomedical fields into a general protocol for identification of imaging system requirements for a targeted application, modelled after a specific example for imaging of a biocomputational microfluidic device that utilizes microorganisms as exploratory problem-solving agents.The following chapters introduce the aforementioned dual-phone system, which uses a phone camera with an external lens for imaging, to achieve a spatial resolution of at least 2 μm, and a large field-of-view of 3.6 × 2.7 mm.For illumination, it uses the screen of another phone to project multi-modal illumination patterns, including but not limited to brightfield, dark-field, Rheinberg illumination, point illumination, fluorescence, and differential phase contrast.Put together, this illumination-imaging system forms a novel, inexpensive, compact, portable, and versatile microscope for use in low-resource environments.It could be used in research, medical, educational, and environmental settings for both qualitative and quantitative imaging of cells, microorganisms, and other micron-sized objects.The adaptability of phone screen illumination allows it to be further integrated into lens-free imaging platforms, as well as conventional microscopes.V. Nicolau, for providing me with this great opportunity to learn, grow, and prove to them and to myself how fulfilling the fruit of perseverance and hard work can be.When days were rough, you helped guide me back onto the right path and regain my discipline.For that, and for your invaluable mentorship, I thank you.Additionally, I would also like to thank my co-supervisor, Sylvain Martel, and my Ph.D. committee members for their precious guidance on my work and future steps.Lastly, I would like to express my gratitude towards the Faculty of Engineering
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".