Image Quality Improvement in Computed and Binary Tomography

This thesis is the summary of the Author's research in the field of Computed and Binary Tomography. Our main aim was to improve reconstruction quality by developing novel algorithms and improving previous approaches in the research fields of selecting the most informative projection angles, automatic selection of the tube voltage of a CT scanner, and binarizing already reconstructed CT slices using Convolutional Neural Networks

Machine learning framework to segment sarcomeric structures in SMLM data

Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network’s internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data

Machine learning framework to segment sarcomeric structures in SMLM data

Object detection is an image analysis task with a wide range of applications, which is difficult to accomplish with traditional programming. Recent breakthroughs in machine learning have made significant progress in this area. However, these algorithms are generally compatible with traditional pixelated images and cannot be directly applied for pointillist datasets generated by single molecule localization microscopy (SMLM) methods. Here, we have improved the averaging method developed for the analysis of SMLM images of sarcomere structures based on a machine learning object detection algorithm. The ordered structure of sarcomeres allows us to determine the location of the proteins more accurately by superimposing SMLM images of identically assembled proteins. However, the area segmentation process required for averaging can be extremely time-consuming and tedious. In this work, we have automated this process. The developed algorithm not only finds the regions of interest, but also classifies the localizations and identifies the true positive ones. For training, we used simulations to generate large amounts of labelled data. After tuning the neural network’s internal parameters, it could find the localizations associated with the structures we were looking for with high accuracy. We validated our results by comparing them with previous manual evaluations. It has also been proven that the simulations can generate data of sufficient quality for training. Our method is suitable for the identification of other types of structures in SMLM data

A Reconstruction-Free Projection Selection Procedure for Binary Tomography Using Convolutional Neural Networks

In discrete tomography sometimes it is necessary to reduce the number of projections used for reconstructing the image. Earlier, it was shown that the choice of projection angles can significantly influence the quality of the reconstructions. In this study, we apply convolutional neural networks to select projections in order to reconstruct the original images from their sinograms with the smallest possible error. The training of neural networks is generally a time-consuming process, but after the network has been trained, the prediction for a previously unseen input is fast. We trained convolutional neural networks using sinograms as input and the desired, algorithmically determined k-best projections as labels in a supervised setting. We achieved a significantly faster projection selection and only a slight increase in the Relative Mean Error (RME)