Tatyana Ivanovska

OTH Amberg-Weiden · EMI

Objective: Menopause is associated with multiple health risks. In several studies, a higher incidence or a higher risk for obstructive sleep apnea (OSA) in post-menopausal than pre-menopausal women is reported. This study was designed to verify such a connection between menopause and OSA in a population-based sample. Methods: For a subsample (N = 1...

Obstructive sleep apnea is a common disorder that leads to sleep fragmentation and is potentially bidirectionally related to a variety of comorbidities, including an increased risk of heart failure and stroke. It is often considered a consequence of anatomical abnormalities, especially in the head and neck, but its pathophysiology is likely to be m...

Purpose Socioeconomic factors are known to modulate health. Concerning sleep apnea, influences of income, education, work, and living in a partnership are established. However, results differ between national and ethnic groups. Results also differ between various clinical studies and population-based approaches. The goal of our study was to determi...

Abstract—The main purpose of our project was to automat- ically delineate parapharyngeal fat pads from magnetic reso- nance imaging (MR) data, since these structures are considered important for diagnosis of obstructive sleep apnea syndrome (OSAS). Here, we investigate the problem, discuss possible data choices, compare 2D and 3D networks, and cons...

Discussion of challenges during data selection, development of the automated pipeline, and its application to numerous MR datasets. https://conferences.eg.org/vcbm2021/wp-content/uploads/sites/16/2021/09/01_ivanovska_poster.pdf

Abstract Purpose The main purpose of this work was to develop an efficient approach for segmentation of structures that are relevant for diagnosis and treatment of obstructive sleep apnea syndrome (OSAS), namely, pharynx, tongue, and soft palate, from mid-sagittal magnetic resonance imaging (MR) data. This framework will be applied to big data acqu...

The first step in automated analysis of medical volumetric data is to detect slices, where specific body parts are located. In our project, we aimed to extract the pelvis region from whole-body CT scans. Two deep learning approaches, namely, an unsupervised slice score regressor, and a supervised slice classification method, were evaluated on a rel...

The first step in automated analysis of medical volumetric data is to detect slices, where specific body parts are located. In our project, we aimed to extract the pelvis regionfrom whole-body CT scans. Two deep learning approaches, namely, an unsupervised slice score regressor, and a supervised slice classification method, were evaluated on a rela...

The manufacturing industry is seeing an increase in demand for more custom-made, low-volume production. This type of production is rarely automated and is to a large extent still performed manually. To keep up with the competition and market demands, manufacturers will have to undertake the effort to automate such manufacturing processes. However,...

Setting up computer vision quality control tasks in a robot workcell is expensive, time consuming, and often requires expert knowledge. In this work, a highly adaptable approach to mitigate this issue is introduced. First, an ontology of atomic building blocks is defined, where each block represents one computer vision algorithm. Second, these bloc...

Purpose. The main purpose of this work is to develop, apply, and evaluate an efficient approach for breast density estimation in magnetic resonance imaging (MRI) data, which contain strong artifacts including intensity inhomogeneities. Methods. We present a pipeline for breast density estimation, which consists of intensity inhomogeneity correction...

Abstract Purpose: To evaluate the effect of geometric distortion on MRI lung volume quantification and evaluate available manual, semi- and fully automated methods for lung segmentation. Methods and Materials: A phantom was scanned with MRI and CT. GD was quantified as difference in phantom’s volume between MRI and CT, with CT as gold standard. Di...

Objectives Whole-body MR imaging is increasingly utilised; although for lung dedicated sequences are often not included, the chest is typically imaged. Our objective was to determine the clinical utility of lung volumes derived from non-dedicated MRI sequences in the population-based KORA-FF4 cohort study. Methods 400 subjects (56.4 ± 9.2 years, 5...

Using 3D anatomical landmarks from adult human head MRIs, we assessed the magnitude of inter-operator differences in Procrustes-based geometric morphometric analyses. An in depth analysis of both absolute and relative error was performed in a subsample of individuals with replicated digitization by three different operators. The effect of inter-ope...

Recently, large population-based studies gain in- creasing focus in the research community. Epidemiological studies acquire numerous data by means of questionnaires and exam- inations. Many of these studies also collect imaging data, for instance, magnetic resonance imaging or ultrasonography from hundreds or even thousands of participants. Here, w...

Background Obstructive sleep apnea (OSA) is a public health problem. Detailed analysis of the para-pharyngeal fat pads can help us to understand the pathogenesis of OSA and may mediate the intervention of this sleeping disorder. A reliable and automatic para-pharyngeal fat pads segmentation technique plays a vital role in investigating larger data...

In this paper, we discuss magnetic resonance (MR) lung imaging and the related image processing tasks from two on-going epidemiological studies conducted in Germany. A modularized system for efficient lung segmentation is proposed and applied for test lung datasets from both studies. The efficiency of the framework is demonstrated by comparison of...

Magnetic resonance imaging (MRI) is a non-radiation based examination method, which gains an increasing popularity in research and clinical settings. Manual analysis of large data volumes is a very time-consuming and tedious process. Therefore, automatic analysis methods are required. This paper reviews different methods that have been recently pro...

Intensity inhomogeneity (bias field) is a common artefact in magnetic resonance (MR) images, which hinders successful automatic segmentation. In this work, a novel algorithm for simultaneous segmentation and bias field correction is presented. The proposed energy functional allows for explicit regularization of the bias field term, making the model...

Breast density measuring the volumetric portion of fibroglandular tissue is considered as an important factor in evaluating breast cancer risk of women. Categorizing breast density into different levels by human observers is time-consuming and subjective, which may result in large inter-reader variability. In this work, we propose a fully automated...

The first step in automated breast density estimation is to extract breast volume of interest, namely, the start and end slice numbers from the whole sequence. We evaluated results produced by two radiologists and developed an automatic strategy for the start and end slice detection. The result comparison showed that it is usually more straightforw...

In our project, we analyse throat structures using magnetic resonance imaging (MRI) to associate anatomic risk factors with sleep related disorders. Pharynx segmentation is the first step in the three-dimensional analysis of throat tissues. We present a pipeline for automatic pharynx segmentation. The automatic part of the approach consists of thre...

Obstructive sleep apnea (OSA) is a public health problem. Volumetric analysis of the upper airways can help us to understand the pathogenesis of OSA. A reliable pharynx segmentation is the first step in identifying the anatomic risk factors for this sleeping disorder. As manual segmentation is a time-consuming and subjective process, a fully automa...

Breast density is a risk factor associated with the development of breast cancer. Usually, breast density is assessed on two dimensional (2D) mammograms using the American College of Radiology (ACR) classification. Magnetic resonance imaging (MRI) is a non-radiation based examination method, which offers a three dimensional (3D) alternative to clas...

Magnetic resonance (MR) images are prone to inhomogeneity artefacts that hinder an efficient automatic segmentation. Existing correction methods are often dependent on initialization and computationally expensive. This paper proposes a novel variational approach for the simultaneous bias field correction and image segmentation together with its eff...

In our project, soft tissue structures of a throat are examined via MRI and anatomic risk factors for sleep related disorders are studied. Segmentation of pharyngeal structures is the first step in three dimensional analysis of throat tissues. We present a pipeline for pharynx segmentation with semi-automatic initialization. The automatic part of t...

Intensity inhomogeneity represents a significant challenge in image processing. Popular image segmentation algorithms produce inadequate results in images with intensity inhomogeneity. Existing correction methods are often computationally expensive. Therefore, efficient implementations for the bias field estimation and inhomogeneity correction are...

One possible measure to increase the medial appeal of table tennis is to slow down the game by using bigger balls or higher nets. Usually, an empirical approach is followed to study the effect of such changes on the players and the game. In this work, a different approach is taken, namely solving numerically the equation of motion for table tennis...

The GPU programmability opens a new perspective for algorithms that have not been studied and used for real applications on commodity state-of-the-art hardware due to their computational expenses. In this paper, we present three implementations of a partitioning algorithm for multi-channel images, which extends an original algorithm for single-chan...

In computer-aided diagnosis of breast MRI, a precise segmentation of the breast is often required as a fundamental step to facilitate further diagnostic tasks, e.g., breast density measurement, lesion detection and automatic reporting. In this work, a fully automatic method dedicated to breast segmentation is proposed, which comprises four major st...

In this paper, we address the problem of multichannel image partitioning and restoration, which includes simultaneous denoising and segmentation processes. We consider a global approach for multichannel image partitioning using minimum description length (MDL). The studied model includes a piecewise constant image representation with uncorrelated G...

In modern epidemiological population-based studies a huge amount of magnetic resonance imaging (MRI) data is analysed. This requires reliable automatic methods for organ extraction. In the current paper, we propose a fast and accurate automatic method for lung segmentation and volumetry. Our approach follows a "coarse-to-fine" segmentation strategy...

A high amount of magnetic resonance imaging (MRI) data is processed in modern epidemiological studies. Reliable and fast automatic segmentation algorithms are required to assist in data analysis. In our project, tracheal dimensions in living patients are studied. We present a fully automated segmentation method for trachea extraction based on inten...

Similar to clinical practise, population-based studies with aclinical-epidemiological focus include imaging techniques to identifymanifest disease and to assess subclinical disease. Even population-basedcross-sectional studies offer various options to address scientificquestions of great clinical relevance, including analysis of referencevalues, pr...

Similar to clinical practise, population-based studies with a clinical-epidemiological focus include imaging techniques to identify manifest disease and to assess subclinical disease. Even population-based cross-sectional studies offer various options to address scientific questions of great clinical relevance, including analysis of reference value...

Quantification of different types of cells is often needed for analysis of histological images. In our project, we compute the relative number of proliferating hepatocytes for the evaluation of the regeneration process after partial hepatectomy in normal rat livers. Our presented automatic approach for hepatocyte (HC) quantification is suitable for...

Quantity of hepatocytes in the liver can reveal a lot of information for medical researchers. In our project, it is needed for evaluation of the liver regeneration rate. In this paper, we present a processing pipeline for automatic counting of hepatocytes from images of histological sections. In particular, we propose to introduce a preprocessing s...

Segmentation and surface extraction from 3D imaging data is an important task in medical applications. When dealing with scalar data such as CT or MRI scans, a simple thresholding in form of isosurface extraction is an often a good choice. Isosurface extraction is a standard tool for visualizing scalar volume data. Its generalization to color data...

Most medical scanning techniques generate scalar fields, for which a large range of segmentation algorithms exists. Some scanning techniques like cryosections, however, generate color data typically stored in RGB format. Since standard segmen-tation algorithms such as isosurface extraction, level-set and region growing methods all have their advant...