Segmentation and quantification of spinal cord gray matter–white matter structures in magnetic resonance images

This thesis focuses on finding ways to differentiate the gray matter (GM) and white matter (WM) in magnetic resonance (MR) images of the human spinal cord (SC). The aim of this project is to quantify tissue loss in these compartments to study their implications on the progression of multiple sclerosis (MS). To this end, we propose segmentation algorithms that we evaluated on MR images of healthy volunteers. Segmentation of GM and WM in MR images can be done manually by human experts, but manual segmentation is tedious and prone to intra- and inter-rater variability. Therefore, a deterministic automation of this task is necessary. On axial 2D images acquired with a recently proposed MR sequence, called AMIRA, we experiment with various automatic segmentation algorithms. We first use variational model-based segmentation approaches combined with appearance models and later directly apply supervised deep learning to train segmentation networks. Evaluation of the proposed methods shows accurate and precise results, which are on par with manual segmentations. We test the developed deep learning approach on images of conventional MR sequences in the context of a GM segmentation challenge, resulting in superior performance compared to the other competing methods. To further assess the quality of the AMIRA sequence, we apply an already published GM segmentation algorithm to our data, yielding higher accuracy than the same algorithm achieves on images of conventional MR sequences. On a different topic, but related to segmentation, we develop a high-order slice interpolation method to address the large slice distances of images acquired with the AMIRA protocol at different vertebral levels, enabling us to resample our data to intermediate slice positions. From the methodical point of view, this work provides an introduction to computer vision, a mathematically focused perspective on variational segmentation approaches and supervised deep learning, as well as a brief overview of the underlying project's anatomical and medical background

Do Central Bank Forecast Errors Contribute to the Missing of Inflation Targets? The Case of the Czech Republic

This paper is primarily concerned with assessing the bias of the CNB’s predictions in relation to undershooting of the inflation target. We conclude that the inflation prediction error has decreased over time. We further point out that GDP growth and interest rates were, respectively, above and below the forecast most of the time, even in a situation of systematic undershooting of the target. Thus, the undershooting cannot be explained with the help of standard demand mechanisms. Positive supply impulses were admittedly underestimated in the past. According to our findings, about half of the apparent target undershooting in 2003 was due to errors in the predictions of exogenous factors (foreign interest rates, GDP, and inflation). As follows from the distribution of the inflation prediction errors across separate price segments, overpredictions of inflation during most of the period under review were due to mistakes in the prediction of food prices and core CPI ex food, while prediction errors in energy prices mostly fostered convergence to the target. The prediction errors in regulated prices acted in both directions.macroeconomics, monetary policy, inflation targeting, forecasting

Growing perfect cubes

AbstractAn (n,a,b)-perfect double cube is a b×b×b sized n-ary periodic array containing all possible a×a×a sized n-ary array exactly once as subarray. A growing cube is an array whose cj×cj×cj sized prefix is an (nj,a,cj)-perfect double cube for j=1,2,…, where cj=njv/3,v=a3 and n1<n2<⋯. We construct the smallest possible perfect double cube (a 256×256×256 sized 8-ary array) and growing cubes for any a

Evaluation of direct subsides granted to agricultural produces in Hajdúböszörmény

The accession of Hungary to the EU can be considered a milestone in the life of domestic agriculture, as currently 70% of the income of agricultural producers is funded by agricultural and rural development subsidies. Besides the timeliness of the topic, it is to be highlighted that agriculture has great traditions in Hajdúböszörmény. As a general objective of the study, the relationship of agricultural producers in Hajdúböszörmény with direct subsidies was determined. In the first part of the research, agricultural subsidisation systems of the European Union and Hungary were processed. Subsequently, with regard to Hajdúböszörmény and based on the subsidy-related data available for the period of 2008-2017, subsidies paid during the last 10 years were demonstrated in various breakdowns (resources, funds, settlements and subsidy type). In addition to the above, measurement of the concentration of direct subsidies was realised by means of three concentration indexes (Lorenz curve, CR concentration, Hirschman-Herfindahl index). &nbsp

Pathology Segmentation using Distributional Differences to Images of Healthy Origin

Fully supervised segmentation methods require a large training cohort of already segmented images, providing information at the pixel level of each image. We present a method to automatically segment and model pathologies in medical images, trained solely on data labelled on the image level as either healthy or containing a visual defect. We base our method on CycleGAN, an image-to-image translation technique, to translate images between the domains of healthy and pathological images. We extend the core idea with two key contributions. Implementing the generators as residual generators allows us to explicitly model the segmentation of the pathology. Realizing the translation from the healthy to the pathological domain using a variational autoencoder allows us to specify one representation of the pathology, as this transformation is otherwise not unique. Our model hence not only allows us to create pixelwise semantic segmentations, it is also able to create inpaintings for the segmentations to render the pathological image healthy. Furthermore, we can draw new unseen pathology samples from this model based on the distribution in the data. We show quantitatively, that our method is able to segment pathologies with a surprising accuracy being only slightly inferior to a state-of-the-art fully supervised method, although the latter has per-pixel rather than per-image training information. Moreover, we show qualitative results of both the segmentations and inpaintings. Our findings motivate further research into weakly-supervised segmentation using image level annotations, allowing for faster and cheaper acquisition of training data without a large sacrifice in segmentation accuracy