Dr. Tatyana Ivanovska

Group(s): Computer Vision
Email:
tatyana.ivanovska@phys.uni-goettingen.de
Phone: +49 551/ 39 10764
Room: E.01.103

Global QuickSearch:   Matches: 0

Search Settings

    Author / Editor / Organization
    Year
    Title
    Journal / Proceedings / Book
    Ivanovska, T. and Herzog, S. and Flores, J. M. and Ciet, P. and Linsen, L. and Duijts, L. and Tiddens, H. and Völzke, H. and Annette Peters, F. W. (2017).
    Potential of Epidemiological Imaging for Image Analysis and Visualization Applications: A Brief Review. In Proceedings of 4th Int.Conf. on Mathematics and Computers in Sciences and Industry (MCSI 2017).
    BibTeX:
    @conference{ivanovskaherzogflores2017,
      author = {Ivanovska, T. and Herzog, S. and Flores, J. M. and Ciet, P. and Linsen, L. and Duijts, L. and Tiddens, H. and Völzke, H. and Annette Peters, F. W.},
      title = {Potential of Epidemiological Imaging for Image Analysis and Visualization Applications: A Brief Review},
      booktitle = {In Proceedings of 4th Int.Conf. on Mathematics and Computers in Sciences and Industry (MCSI 2017)},
      year = {2017},
      abstract = Recently, large population-based studies gain increasing focus in the research community. Epidemiological studies acquire numerous data by means of questionnaires and examinations. Many of these studies also collect imaging data, for instance, magnetic resonance imaging or ultrasonography from hundreds or even thousands of participants. Here, we consider several on-going epidemiological studies conducted in Europe as well as challenges of subsequent image analysis and visualization of heterogeneous data, which were obtained within these studies. In particular, the main focus is on airway extraction tasks and the visual analytics problems. Available solutions and future directions for computer science specialists are presented and analyzed in terms of user-friendliness, speed, and efficiency.}}
    		
    Abstract: Recently, large population-based studies gain increasing focus in the research community. Epidemiological studies acquire numerous data by means of questionnaires and examinations. Many of these studies also collect imaging data, for instance, magnetic resonance imaging or ultrasonography from hundreds or even thousands of participants. Here, we consider several on-going epidemiological studies conducted in Europe as well as challenges of subsequent image analysis and visualization of heterogeneous data, which were obtained within these studies. In particular, the main focus is on airway extraction tasks and the visual analytics problems. Available solutions and future directions for computer science specialists are presented and analyzed in terms of user-friendliness, speed, and efficiency.
    Review:
    Ivanovska, T. and Ciet, P. and Perez-Rovira, A. and Nguyen, A. and Tiddens, H. and Duijts, L. and Bruijne, M. and Wörgötter, F. (2017).
    Fully Automated Lung Volume Assessment from MRI in a Population-based Child Cohort Study. Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP), 53-58. DOI: 10.5220/0006075300530058.
    BibTeX:
    @conference{ivanovskacietperezrovira2017,
      author = {Ivanovska, T. and Ciet, P. and Perez-Rovira, A. and Nguyen, A. and Tiddens, H. and Duijts, L. and Bruijne, M. and Wörgötter, F.},
      title = {Fully Automated Lung Volume Assessment from MRI in a Population-based Child Cohort Study},
      pages = {53-58},
      booktitle = {Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISAPP)},
      year = {2017},
      organization = {ScitePress},
      publisher = {ScitePress},
      doi = 10.5220/0006075300530058},
      abstract = In this work, a framework for fully automated lung extraction from magnetic resonance imaging (MRI) inspiratory data that have been acquired within a on-going epidemiological child cohort study is presented. The methods main steps are intensity inhomogeneity correction, denoising, clustering, airway extraction and lung region refinement. The presented approach produces highly accurate results (Dice coefficients 95%), when compared to semi-automatically obtained masks, and has potential to be applied to the whole study data.}}
    		
    Abstract: In this work, a framework for fully automated lung extraction from magnetic resonance imaging (MRI) inspiratory data that have been acquired within a on-going epidemiological child cohort study is presented. The methods main steps are intensity inhomogeneity correction, denoising, clustering, airway extraction and lung region refinement. The presented approach produces highly accurate results (Dice coefficients 95%), when compared to semi-automatically obtained masks, and has potential to be applied to the whole study data.
    Review:
    Gressmann, F. and Lüddecke, T. and Ivanovska, T. and Schoeler, M. and Wörgötter, F. (2017).
    Part-driven Visual Perception of 3D Objects. Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017), 370-377. DOI: 10.5220/0006211203700377.
    BibTeX:
    @conference{gressmannlueddeckeivanovska2017,
      author = {Gressmann, F. and Lüddecke, T. and Ivanovska, T. and Schoeler, M. and Wörgötter, F.},
      title = {Part-driven Visual Perception of 3D Objects},
      pages = {370-377},
      booktitle = {Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, (VISIGRAPP 2017)},
      year = {2017},
      organization = {ScitePress},
      publisher = {ScitePress},
      doi = 10.5220/0006211203700377},
      abstract = During the last years, approaches based on convolutional neural networks (CNN) had substantial success in visual object perception. CNNs turned out to be capable of extracting high-level features of objects, which allow for fine-grained classification. However, some object classes exhibit tremendous variance with respect to their instances appearance. We believe that considering object parts as an intermediate representation could be helpful in these cases. In this work, a part-driven perception of everyday objects with a rotation estimation is implemented using deep convolution neural networks. The used network is trained and tested on artificially generated RGB-D data. The approach has a potential to be used for part recognition of realistic sensor recordings in present robot systems.}}
    		
    Abstract: During the last years, approaches based on convolutional neural networks (CNN) had substantial success in visual object perception. CNNs turned out to be capable of extracting high-level features of objects, which allow for fine-grained classification. However, some object classes exhibit tremendous variance with respect to their instances appearance. We believe that considering object parts as an intermediate representation could be helpful in these cases. In this work, a part-driven perception of everyday objects with a rotation estimation is implemented using deep convolution neural networks. The used network is trained and tested on artificially generated RGB-D data. The approach has a potential to be used for part recognition of realistic sensor recordings in present robot systems.
    Review:
    Shahid, M. L. U. R. and Chitiboi, T. and Ivanovska, T. and Molchanov, V. and Völzke, H. and Linsen, L. (2017).
    Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification. BMC Medical Imaging, 15, 17, 1. DOI: 10.1186/s12880-017-0179-7.
    BibTeX:
    @article{shahidchitiboiivanovska2017,
      author = {Shahid, M. L. U. R. and Chitiboi, T. and Ivanovska, T. and Molchanov, V. and Völzke, H. and Linsen, L.},
      title = {Automatic MRI segmentation of para-pharyngeal fat pads using interactive visual feature space analysis for classification},
      pages = {15},
      journal = {BMC Medical Imaging},
      year = {2017},
      volume= {17},
      number = {1},
      url = http://dx.doi.org/10.1186/s12880-017-0179-7},
      doi = 10.1186/s12880-017-0179-7},
      abstract = 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 bases to identify the anatomic risk factors for the OSA.}}
    		
    Abstract: 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 bases to identify the anatomic risk factors for the OSA.
    Review:
    Ivanovska, T. and Hegenscheid, K. and Laqua, R. and Gläser, S. and Ewert, R. and Völzke, H. (2016).
    Lung Segmentation of MR Images: A Review. Visualization in Medicine and Life Sciences III: Towards Making an Impact, 3-24. DOI: 10.1007/978-3-319-24523-2_1.
    BibTeX:
    @inbook{ivanovskahegenscheidlaqua2016,
      author = {Ivanovska, T. and Hegenscheid, K. and Laqua, R. and Gläser, S. and Ewert, R. and Völzke, H.},
      title = {Lung Segmentation of MR Images: A Review},
      pages = {3-24},
      booktitle = {Visualization in Medicine and Life Sciences III: Towards Making an Impact},
      year = {2016},
      editor = {Linsen, Lars and Hamann, Bernd and Hege, Hans-Christian},
      publisher = {Springer International Publishing},
      url = https://link.springer.com/chapter/10.1007%2F978-3-319-24523-2_1},
      doi = 10.1007/978-3-319-24523-2_1},
      abstract = 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 proposed for automatic and semi-automatic lung segmentation from magnetic resonance imaging data. These techniques include thresholding, region growing, morphological operations, active contours, level sets, and neural networks. We also discuss the methodologies that have been utilized for performance and accuracy evaluation of each method.}}
    		
    Abstract: 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 proposed for automatic and semi-automatic lung segmentation from magnetic resonance imaging data. These techniques include thresholding, region growing, morphological operations, active contours, level sets, and neural networks. We also discuss the methodologies that have been utilized for performance and accuracy evaluation of each method.
    Review:
    Ivanovska, T. and Pomschar, A. and Lorbeer, R. and Kunz, W. and Schulz, H. and Hetterich, H. and Völzke, H. and Bamber, F. and Peters, A. and Wörgötter, F. (2016).
    Efficient population-based big MR data analysis: a lung segmentation and volumetry example. In Proceedings of Sixth International Workshop on Pulmonary Image Analysis (PIA) at MICCAI 2016.
    BibTeX:
    @conference{ivanovskapomscharlorbeer2016,
      author = {Ivanovska, T. and Pomschar, A. and Lorbeer, R. and Kunz, W. and Schulz, H. and Hetterich, H. and Völzke, H. and Bamber, F. and Peters, A. and Wörgötter, F.},
      title = {Efficient population-based big MR data analysis: a lung segmentation and volumetry example},
      booktitle = {In Proceedings of Sixth International Workshop on Pulmonary Image Analysis (PIA) at MICCAI 2016},
      year = {2016},
      location = {Athens, Greece},
      abstract = 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 automatically computed results to the manually created ground truth masks. The presented pipeline allows one to obtain highly accurate segmentation results even for MR data with lower quality.}}
    		
    Abstract: 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 automatically computed results to the manually created ground truth masks. The presented pipeline allows one to obtain highly accurate segmentation results even for MR data with lower quality.
    Review:

    © 2011 - 2016 Dept. of Computational Neuroscience • comments to: sreich _at_ gwdg.de • Impressum / Site Info