The analysis of wide-angle cellular light scattering patterns is a challenging problem. Small changes to the organization, orientation, shape, and optical properties of scatterers and scattering populations can significantly alter their complex two-dimensional scattering signatures. Because of this, it is difficult to find methods that can identify medically relevant cellular properties while remaining robust to experimental noise and sample-to-sample differences. It is an important problem. Recent work has shown that changes to the internal structure of cells---specifically, the distribution and aggregation of organelles---can indicate the progression of a number of common disorders, ranging from cancer to neurodegenerative disease, and can also predict a patient's response to treatments like chemotherapy. However, there is no direct analytical solution to the inverse wide-angle cellular light scattering problem, and available simulation and interpretation methods either rely on restrictive cell models, or are too computationally demanding for routine use.
This dissertation addresses these challenges from a computational vantage point. First, it explores the theoretical limits and optical basis for wide-angle scattering pattern analysis. The result is a rapid new simulation method to generate realistic organelle scattering patterns without the need for computationally challenging or restrictive routines. Pattern analysis, image segmentation, machine learning, and iterative pattern classification methods are then used to identify novel relationships between wide-angle scattering patterns and the distribution of organelles (in this case mitochondria) within a cell. Importantly, this work shows that by parameterizing a scattering image it is possible to extract vital information about cell structure while remaining robust to changes in organelle concentration, effective size, and random placement. The result is a powerful collection of methods to simulate and interpret experimental light scattering signatures. This gives new insight into the theoretical basis for wide-angle cellular light scattering, and facilitates advances in real-time patient care, cell structure prediction, and cell morphology research.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/774 |
| Date | 11 1900 |
| Creators | Pilarski, Patrick Michael |
| Contributors | Backhouse, Christopher J. (Electrical and Computer Engineering), Musilek, Petr (Electrical and Computer Engineering), Reformat, Marek (Electrical and Computer Engineering), Cockburn, Bruce (Electrical and Computer Engineering), Bischof, Walter F. (Computing Science), Wheeler, Arron (Chemisty, University of Toronto) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 7660585 bytes, application/pdf |
| Relation | Patrick M. Pilarski and Christopher J. Backhouse, "A method for cytometric image parameterization", Optics Express, Vol. 14(26), 12720-12743 (2006)., Patrick M. Pilarski, Xuan-Tao Su, D. Moira Glerum, and Christopher J. Backhouse, "Rapid simulation of wide-angle scattering from mitochondria in single cells", Optics Express, Vol. 16(17), 12819-12834 (2008)., Patrick M. Pilarski, Xuan-Tao Su, D. Moira Glerum, and Christopher J. Backhouse, "Computational analysis of mitochondrial placement and aggregation effects on wide-angle cell scattering patterns,'' Proceedings of SPIE, Vol. 7187, 71870J, 12 pages (2009). |
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