This thesis deals with developing of image processing algorithm for robots controlled by informations taken from visual system. Core of the used system constitutes low-budget platform Raspberry Pi. Before the development of algorithm there is a series of test for image processing which discovers possibilities of used platform. Problem solution is divided to several parts. Limited performance frequently leads to individual problem solving. Afterall is shown sensitivity and performance analysis of developed solution.

This study aims at the development of materials for biodegradable fiducial markers for X-ray based medical imaging and their anchorage in soft tissue. Towards this goal a degradable polymer matrix of poly(L-lactide-co-ε-caprolactone) (P[LAcoCL]) was combined with barium sulfate (BaSO₄) and hydroxyapatite (HAp) as radio-opaque fillers. Low pressure plasma treatment was applied to the composite materials to improve cell adhesion and subsequent tissue integration. In particular, the effects of oxygen and ammonia plasmas were evaluated and compared using X-ray photoelectron spectroscopy, atomic force microscopy and dynamic water contact angle measurements as well as in vitro studies using the murine fibroblast cell line L929. To exclude the cytotoxic effects of degradation products of P[LAcoCL] and released BaSO₄ or HAp cytotoxicity assays with the degradation products of the composite materials were conducted. The results obtained by this broad range of analytical techniques suggest the application of composites of P[LAcoCL] with BaSO₄ and HAp as promising material systems for innovative fiducial markers for soft tissue in X-ray based medical imaging.

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<p>    Adaptive MCMC is advantageous over traditional MCMC due to its ability to automatically adjust its proposal distributions during the sampling process, providing improved sampling efficiency and faster convergence to the target distribution, especially in complex or high-dimensional problems. However, designing and validating the adaptive scheme cautiously is crucial to ensure algorithm validity and prevent the introduction of biases. This dissertation focuses on the use of Adaptive MCMC for deep learning, specifically addressing the mode collapse issue in Generative Adversarial Networks (GANs) and implementing Fiducial inference, and its application to Causal inference in individual treatment effect problems.</p> <p><br></p> <p>    First, GAN was recently introduced in the literature as a novel machine learning method for training generative models. However, GAN is very difficult to train due to the issue of mode collapse, i.e., lack of diversity among generated data. We figure out the reason why GAN suffers from this issue and lay out a new theoretical framework for GAN based on randomized decision rules such that the mode collapse issue can be overcome essentially. Under the new theoretical framework, the discriminator converges to a fixed point while the generator converges to a distribution at the Nash equilibrium.</p> <p><br></p> <p>    Second, Fiducial inference was generally considered as R.A. Fisher's a big blunder, but the goal he initially set, <em>making inference for the uncertainty of model parameters on the basis of observations</em>, has been continually pursued by many statisticians. By leveraging on advanced statistical computing techniques such as stochastic approximation Markov chain Monte Carlo, we develop a new statistical inference method, the so-called extended Fiducial inference, which achieves the initial goal of fiducial inference. </p> <p><br></p> <p>    Lastly, estimating ITE is important for decision making in various fields, particularly in health research where precision medicine is being investigated. Conditional average treatment effect (CATE) is often used for such purpose, but uncertainty quantification and explaining the variability of predicted ITE is still needed for fair decision making. We discuss using extended Fiducial inference to construct prediction intervals for ITE, and introduces a double neural net algorithm for efficient prediction and estimation of nonlinear ITE.</p>

Radiotherapy is a common and useful method for treating prostate cancer, often using gold fiducial markers in the prostate as guidance. However, when there is a high risk of lymph node involvement, the independent motion of volumes causes complications in patient positioning since there is a choice between position against the gold fiducial markers or the bone anatomy. This leads to expansion of margins for either the prostate or the pelvic lymph nodes. In this thesis two different treatment strategies were performed and compared against given treatment plans. The purpose was to evaluate the standard treatment and to be able to recommend a new clinical approach for treatment of high-risk prostate cancer. Nine high-risk prostate cancer patients with their given treatment plans were used as a baseline. The patients underwent a planning CT and five CBCTs during the treatment. Two new treatment plan setups were done, a robust treatment and a sequential treatment with three and nine different plans respectively. The baseline and the robust treatment used gold fiducial markers as reference, with a prescribed dose of 2.20 Gy over 35 fractions with a VMAT. The sequential treatment used both gold fiducial markers and bone anatomy as reference, done by 35 fractions with a prescribed dose of 0.6 Gy with a single arc and 1.6 Gy with a dual arc respectively. A total of thirteen different treatment plan setups for each patient were simulated 100 times each, resulting in 11700 simulated treatments in total. The resulting simulated treatments were evaluated by the percentage passing nine different clinical goals, as well as dose and percentage volume averages for these goals. The results from the simulated robust treatments showed a decrease in percentage passing and D98 for the prostate and an increase in percentage passing and D98 for the lymph nodes and vesicles compared to the baseline. An increase in percentage passing and D98 was seen in the sequential treatment strategy for both targets compared to the baseline. The rectum had a larger percentage passing the clinical goals and a lower V69, V74 and V59 for both the robust and sequential treatment strategies. The D2 for the external were lower in the robust treatment strategy but higher in the sequential treatment strategy, while the D2 to the femoral heads were lower for both compared to the baseline treatment strategy. In conclusion, an improved dose coverage was seen in the sequential strategy with good sparing of risk organs. The robust treatment strategy showed promising results for sparing risk organs, but with a less robust dose coverage of the prostate.