Medycyna regeneracyjna w leczeniu nietrzymania moczu a technika i sposoby podania materiału komórkowego do zwieracza cewki moczowej

The aim of the work is to present regenerative medicine achievement as an alternative SUI treatment and the variety of injected cells type as well as injection techniques itself with the analysis of their quality and possible the mechanism in which they reduce urinary incontinence symptoms. For over a decade numerous authors declare use of different type of autologous mesenchymal-derived stem cells (AMDC) in male and female SUI. The leakage improvement reached 80%, despite the number of injected cells as well as the injection technique. Important subject in the AMDC treatment is the precise cell material injection into the selected spot which might be possible with the use of the endoscopic assisting robot. The robotic supported system for cells procedure might bring the missing percentage in reaching the goal in SUI treatment.  Celem pracy jest przedstawienie osiągnięć medycyny regeneracyjnej jako alternatywnej terapii w wysiłkowym nietrzymaniu moczu (WNM). Dokonany przegląd literatury problemu wskazuje na różnorodność podawanych linii komórkowych, ich rodzaju, jak i samej techniki iniekcji z analizą jakości i ewentualnego mechanizmu, w którym zmniejszeniu uległy objawy nietrzymania moczu. Od ponad dziesięciu lat wielu autorów deklaruje korzystanie z różnych rodzajów autologicznych mezenchymalnych komórek macierzystych (AMDC autologoue muscle derived stem cells) w leczeniu mężczyzn i kobiet z WNM. Poprawa utrzymania moczu sięga wg niektórych źródeł 80%, niezależnie od liczby wstrzykniętych komórek, oraz sposobu ich podania. Ważnym zagadnieniem w leczeniu z użyciem AMDC, jest precyzyjne podanie materiału komórkowego do wybranego miejsca (zwieracza cewki moczowej), które można udoskonalić stosując endoskopowo wspomagany system robota. Zastosowanie ww systemu mogłoby poprawić technikę podania materiału komórkowego i zobiektywizować wyniki leczenia.

Medical Image Segmentation by Deep Convolutional Neural Networks

Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs