Browsing by Subject "Cell Detection"

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Open Access
Automated cell analysis in microscopy images
(2018-09) Koyuncu, Can Fahrettin
High-throughput microscopy systems have become popular recently, which facilitate to acquire boundless microscopy images without requiring human intervention. However, the analysis of such amount of images using conventional methods is nearly impractical since the analysis can take up to months. Additionally, a considerable amount of observer variability may occur since the analysis completely relies on interpretation of the analysts. As a remedy for that, automated decision support systems, which are objective and rapid, have gained more attention. Since these systems conduct analyses at cellular level, they require a cell segmentation model, results of which directly affect the performance of the entire system. There are several challenges in cell segmentation, each of which should be addressed carefully in order to have an accurate cell segmentation model. One challenge is that cells can be grown in multilayer on the plate, which makes them appear as clusters on the image. Segmentation of these cells requires extra effort since they should be splitted from each other. Another challenge is the imperfections on the image such as inhomogeneities of pixel intensities in a cell and insu cient pixel intensity differences at the border of overlapping cells. Yetanother challenge is the heterogeneity in the morphological characteristics of cells. Depending on cell line types, cells may appear in various outlooks. Developing a generic cell segmentation model, which can handle different cells' outlooks and imperfections, is an open and challenging problem. In order to tackle with these challenges, we deal with the cell segmentation problem in two parts: (1)We focus on finding a new representation for microscopy images, helping us simplify the cell segmentation problem, so that imperfections in cells and inhomogeneities in their visual properties can be alleviated, and cell locations can be emphasized better. (2) We focus on developing a more advanced cell segmentation method, with the motivation that it is almost impossible to obtain a perfect representation in practice. Thus, we work on developing more sophisticated cell segmentation techniques that overcome deficiencies on the representation. To this end, this thesis introduces three new cell segmentation models, two of which introduce a new cell representation technique as well. In our experiments, we tested our algorithms on various microscopy images obtained under the uorescence and phase contrast microscopies and compared them with the previous cell segmentation methods. Our experiments show that the proposed algorithms are more effective in segmenting cells and more robust to the aforementioned challenges.
Open Access
A novel analogy: application of higher-order mode theory in the mechanical domain to the electromagnetic domain
(2018-08) Kelleci, Mehmet
It is crucial to engineer novel detection schemes that can extract information pertinent to the morphological properties of the analytes for widespread usage of lab-on-a-chip technology. Within the scope of this thesis, a novel method that is originated in mechanical domain based on NEMS resonators is adapted to electromagnetic domain with employment of electromagnetic resonators operate in microwave regime. The viability of the proposed method is assessed both by experiments and simulations. The designed micro uidic channel embedded microstrip resonator is driven at its rst two resonant modes simultaneously by a phase-locked loop to detect the analyte passage events within the channel. The attained resolution is 2x10􀀀8 for both modes at the response time in terms of allan deviation. With the detection scheme we constructed, the location and electrical volume of the microdroplets and cells are obtained. It is shown that the two-mode detection scheme based on microwave resonators can be extended to applications that exploits even higher-order modes to obtain the size, orientation, skewness and permittivity information of the target analytes. Morevover, the framework presented here forms a base for a novel imaging application that can be alternative to optical microscopy.