Microscopy image processing is critical for precision oncology and immunotherapy, two approaches in cancer treatment often combined to enhance patient outcomes. Numerous scientists have studied the effects of drugs on the immune system and tumors. To quantify the impact of various drugs on different immune and cancer cell types, medical researchers conduct ex vivo assays. In these assays, patient-derived live cells are placed in an artificial microenvironment where drug responses are monitored over time. Brightfield microscopy captures one image every half hour of numerous patient-derived cells in an ex vivo reconstruction of the tumor microenvironment treated with 31 drugs for up to six days. These images are used to quantify the response of different cells to various drugs. However, tracking thousands of cells in low-resolution, low-frame-rate images remains challenging. Existing digital image processing algorithms require high-resolution images involving very few cells from homogeneous cell line populations. In this work, we propose three novel frameworks to track cancer cells, capture their behavior, and quantify cell viability to inform clinical decisions in a high-throughput manner.

Cell detection using image sensors is a novel and promising technique that can be used for diagnostic applications in medicine. For this purpose, cell detection studies with shadowing method are performed with yeast cells (Saccharomyces cerevisiae) using an 32&times / 32 complementary metal oxide semiconductor (CMOS) image sensor that is sensitive to optical illumination. Cells that are placed zero distance from the sensor surface are detected using the image sensor which is illuminated with four fixed leds to maintain fixed illumination levels in each test. Cells are transferred to the sensor surface with drying the medium they are in, which is phosphate buffered saline (PBS) solution. Yeast cells that are zero distance from the surface are detected with a detection rate of 72%. Then, MCF-7 (breast cancer) cells are detected with the same sensor when the PBS solution is about to dry. To investigate the detection capability of the sensor while the cells are in the PBS solution, the sensor surface is coated with gold in order to immobilize the surface with antibodies. With immobilizing antibodies, cells are thought to be bound to the surface achieving zero distance to the sensor surface. After coating gold, antibodies are immobilized, and same tests are done with MCF-7 cells. In the PBS solution, no sufficient results are obtained with the shadowing technique, but sufficient results are obtained when the solution is about to dry. After achieving cell detection with the image sensor, a similar but large format image sensor is designed. The designed CMOS image sensor has 160&times / 128 pixel array with 15&micro / m pitch. The pixel readout allows capacitive and optical detection. Thus, both DNA and cell detection are possible with this image sensor. The rolling line shutter mode is added for reducing further leakage at pixel readout. Addressing can be done which means specific array points can be investigated, and also array format can be changed for different size cells. The frame rate of the sensor can be adjusted allowing the detection of the fast moving cell samples. All the digital inputs of the sensor can be adjusted manually for the sake of flexibility. A large number of cells can be detected with using this image sensor due to its large format.

Photonic crystals serve as powerful building blocks for the development of lab-on-chip devices. Currently they are used for a wide range of miniaturized optical components such as extremely compact waveguides to refractive-index based optical sensors. Here we propose a new technique for analyzing and characterizing cells through the design of a micro-flow cytometer using photonic crystals. While lab scale flow cytometers have been critical to many developments in cellular biology they are not portable, difficult to use and relatively expensive. By making a miniature sensor capable of replicating the same functionality as the large scale units with photonic crystals, we hope to produce a device that can be easily integrated into a lab-on-chip and inexpensively mass produced for use outside of the lab. Using specialized FDTD software, the proposed technique has been studied, and multiple important flow cytometry functions have been established. As individual cells flow near the crystal surface, transmission of light through the photonic crystal is influenced accordingly. By analyzing the resulting changes in transmission, information such as cell counting and shape characterization have been demonstrated. Furthermore, correlations for simultaneously determining the size and refractive indices of cells has been shown by applying the statistical concepts of central moments.

Nuclei/Cell detection is usually a prerequisite procedure in many computer-aided biomedical image analysis tasks. In this thesis we propose two automatic nuclei/cell detection frameworks. One is for nuclei detection in skeletal muscle fiber images and the other is for brain tumor histopathological images. For skeletal muscle fiber images, the major challenges include: i) shape and size variations of the nuclei, ii) overlapping nuclear clumps, and iii) a series of z-stack images with out-of-focus regions. We propose a novel automatic detection algorithm consisting of the following components: 1) The original z-stack images are first converted into one all-in-focus image. 2) A sufficient number of hypothetical ellipses are then generated for each nuclei contour. 3) Next, a set of representative training samples and discriminative features are selected by a two-stage sparse model. 4) A classifier is trained using the refined training data. 5) Final nuclei detection is obtained by mean-shift clustering based on inner distance. The proposed method was tested on a set of images containing over 1500 nuclei. The results outperform the current state-of-the-art approaches. For brain tumor histopathological images, the major challenges are to handle significant variations in cell appearance and to split touching cells. The proposed novel automatic cell detection consists of: 1) Sparse reconstruction for splitting touching cells. 2) Adaptive dictionary learning for handling cell appearance variations. The proposed method was extensively tested on a data set with over 2000 cells. The result outperforms other state-of-the-art algorithms with F1 score = 0.96.

This diploma thesis deals with analysing of modern methods for cell detection, visualization and quantification in 3D space. The first section deals with optical methods for cells detection. There is detailed discussion about cell labeling and detection on confocal microscopy. There is also description about developed algorithm for whole cell volume quantification from microscopy images. This could made a comparsion of fluorescence signal according to time of cell labeling and according to cell shapes. There was also optimalization of handmade tissue phantoms visualization. It could be compared a possibilities of cell detections in these phantoms by confocal microscopy and OCT. It was also implemented algorithm for quantification of cells from OCT images. Besides confocal microscopy and OCT cells are also analyzed by other methods. The last part is the Conclusion of results and comparison of used methods.

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This thesis reports design and implementation of a MEMS based resonant mass sensor for cell detection applications. The main objective of the thesis is the real-time detection of captured cells inside liquid medium and obtaining the detection results by electronic means, without the aid of any external optical instruments. A new resonant mass sensor architecture is presented that has various advantages over its conventional counterparts. The device oscillates in the lateral direction, eliminating squeeze film damping. A thin parylene layer coated on the device prevents liquids from entering the narrow gaps of the device, further improving the quality factor. The resonator is embedded on the floor of a microchannel. A gold film on the proof mass facilitates antibody based cell capture on the device. Theoretical background regarding resonator operation is investigated. Various resonator designs are presented, taking into account design trade-offs, application v considerations, and fabrication limitations. The design procedure is verified with MATLAB Simulink modeling results and finite element simulations. A new process flow has been developed for resonator fabrication, combining SOI, glass, and polymer micromachining. Modifications have been done on the flow for the solution of problems encountered during device fabrication. Each device has a foot print area of 1.5 x 0.5 cm2. The majority of this area is occupied by fluidic connections and reservoirs. Resonance characterization results in air and water have shown that there is significant quality factor enhancement with the parylene coating method. The quality factor decreases to only 170 in water from 610 in air, when the resonator is coated with a thin layer of parylene. Uniformity and linearity tests revealed that the devices have a standard deviation of only 1.9% for different analyte capture sites and an R2 of 0.997 for mass loads as high as 2.7 ng. Detection of Saccharomyces cerevisiae type yeast cells has been done using the resonators. Mass measurement of single yeast cell (13 pg) and yeast clusters (102 pg) have been performed. Antibody and thiol-gold chemistry based Candida Albicans type bacteria capture and detection has also been made in both air and water environments. The mass of several captured bacterial cells in air has been measured as 95pg. Two bacterial cells have been captured on one device inside water and their mass has been measured as 85 pg. It is worthy to note that all mass measurements are consistent with theoretical expectations.

This thesis reports design and implementation of a MEMS based gravimetric resonator for cytometry applications. There are mainly two objectives of this thesis / to enable in-flow analysis and to perform closed loop operation that does not require any additional processing or equipment. A novel MEMS based resonator with in-flow capabilities is proposed for detection of agents inside micro channels. High resolution of mass detection inside micro channels is planned to be succeeded with lateral motion in the micro channel floor. The idea embedding lateral resonators emerges from decreasing squeeze film damping during the motion of the resonator. Lateral motion is supported by hydrophobic parylene coating to decrease the damping. Theory and design of the gravimetric resonators are explained and the fabrication flow is constructed and performed successfully by combining SOI, SOG and polymer micro fabrication techniques. Problems during the fabrication are overcome and optimized flow is presented. The devices have a foot print area of 1.5 x 0.5 cm2 which is mainly composed of reservoirs for fluidic connections. Ten types of devices are designed according to their mass sensitivities and compliances. Trade offs between frequency, injected current, and compliance are analyzed successfully by taking also the performance parameters of the interface electronics in to account. Test results reveal that single latex bead with 3 &micro / m diameter and 14.127 pg mass can be sensed successfully and mass sensitivity is measured to be 5.91 fg/Hz for this type of device.

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