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Footage of organoids taken by means of fluorescence microscopy and segmented as well as triangulated by image analysis software like LimeSeg and Mastodon often needs to be visualized in aesthetic manner for presentation of the results in scientific papers, talks and demonstrations. The goal of this work was to create a simple to use addon “Biobox” for the open source 3D – visualization package “Blender” which would allow to import triangulated 3D data with animation over time (4D), produced by image analysis software, and optimize it for efficient usage. ”Biobox” offers several visualization tools for the creation of rendered images and animation videos by biologists.
The optimization of imported data was performed by using Blender intern modifiers. The optimized data can then be visualized by using several tools built for visualizing the organoid in frozen, animated and semi-transparent manners. A dynamic link for object selection and dynamic data exchange between Blender and Mastodon was developed. Additionally, a user interface was developed for manual correction errors of segmentation and steering the object detection algorithms of LimeSeg. The benchmark of the developed addon “Biobox” was performed on real scientific data. The benchmark test demonstrated that developed optimization result in significant (~5 fold) decrease of RAM usage and acceleration of visualization more than 160 times.
In this thesis two novel methods for removing undesired background illumination are de-veloped. These include a wavelet analysis based approach and an enhancement of a deep learning method. These methods have been compared with conventional methods, using real confocal microscopy images and synthetic generated microscopy images. These synthetic images were created utilizing a generator introduced in this thesis.
A relatively new research field of neurosciences, called Connectomics, aims to achieve a full understanding and mapping of neural circuits and fine neuronal structures of the nervous system in a variety of organisms. This detailed information will provide insight in how our brain is influenced by different genetic and psychiatric diseases, how memory traces are stored and ageing influences our brain structure. It is beyond question that new methods for data acquisition will produce large amounts of neuronal image data. This data will exceed the zetabyte range and is impossible to annotate manually for visualization and analysis. Nowadays, machine learning algorithms and specially deep convolutional neuronal networks are heavily used in medical imaging and computer vision, which brings the opportunity of designing fully automated pipelines for image analysis. This work presents a new automated workflow based on three major parts including image processing using consecutive deep convolutional networks, a pixel-grouping step called connected components and 3D visualization via neuroglancer to achieve a dense three dimensional reconstruction of neurons from EM image data.
Viele Regulationsprozesse der Lungenentwicklung sind bisher nicht genau bekannt. Neue Möglichkeiten, diese zu untersuchen, liefert das Clearing von Gewebe in Kombination mit Antikörperfärbungen und 3D-Bildgebungsverfahren. Diese erlauben es, die Organogenese sowie die Expression von Transkriptionsfaktoren, wie z.B. Sox9, in drei Dimensionen zu verfolgen. Im Rahmen dieser Bachelorarbeit wurden die Methoden Clear T2, RTF und CLARITY angewendet und mit verschiedenen Färbungen kombiniert. Dabei wurden hauptsächlich Lungenproben von Ratten verwendet. Die Ergebnisse wurden am konfokalen Laser-Scanning-Mikroskop betrachtet und ausgewertet. Dabei wurden Einflüsse der Clearing-Methoden auf die Qualität der Aufnahmen, aber auch auf die Färbungen beobachtet. CLARITY ermöglichte durch das Hydrogel eine bessere Diffusion der Moleküle in die Probe. Durch das Clearing kam es allerdings zu Veränderungen der Gewebestruktur. Clear T2 und RTF zeigten eine sehr gute Erhaltung der Struktur, jedoch stellte die Penetration größerer Moleküle in das Gewebe eine Herausforderung dar. Es wurde deutlich, dass die Clearing-Methode je nach Fragestellung gewählt werden muss.