| 1. |
- Lidayová, Kristína, et al.
(author)
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Airway-tree segmentation in subjects with acute respiratory distress syndrome
- 2017
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In: 20th Scandinavian Conference on Image Analysis, SCIA 2017. - Cham : Springer. - 9783319591285 ; , s. 76-87
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Conference paper (peer-reviewed)abstract
- Acute respiratory distress syndrome (ARDS) is associated with a high mortality rate in intensive care units. To lower the number of fatal cases, it is necessary to customize the mechanical ventilator parameters according to the patient’s clinical condition. For this, lung segmentation is required to assess aeration and alveolar recruitment. Airway segmentation may be used to reach a more accurate lung segmentation. In this paper, we seek to improve lung segmentation results by proposing a novel automatic airway-tree segmentation that is able to address the heterogeneity of ARDS pathology by handling various lung intensities differently. The method detects a simplified airway skeleton, thereby obtains a set of seed points together with an approximate radius and intensity range related to each of the points. These seeds are the input for an onion-kernel region-growing segmentation algorithm where knowledge about radius and intensity range restricts the possible leakage in the parenchyma. The method was evaluated qualitatively on 70 thoracic Computed Tomography volumes of subjects with ARDS, acquired at significantly different mechanical ventilation conditions. It found a large proportion of airway branches including tiny poorly-aerated bronchi. Quantitative evaluation was performed indirectly and showed that the resulting airway segmentation provides important anatomic landmarks. Their correspondences are needed to help a registration-based segmentation of the lungs in difficult ARDS cases where the lung boundary contrast is completely missing. The proposed method takes an average time of 43 s to process a thoracic volume which is valuable for the clinical use.
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| 2. |
- Lidayová, Kristína, et al.
(author)
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Classification of cross-sections for vascular skeleton extraction using convolutional neural networks
- 2017
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In: 21st Annual Conference on Medical Image Understanding and Analysis, MIUA 2017. - Cham : Springer. - 9783319609638 ; , s. 182-194
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Conference paper (peer-reviewed)abstract
- Recent advances in Computed Tomography Angiography provide high-resolution 3D images of the vessels. However, there is an inevitable requisite for automated and fast methods to process the increased amount of generated data. In this work, we propose a fast method for vascular skeleton extraction which can be combined with a segmentation algorithm to accelerate the vessel delineation. The algorithm detects central voxels - nodes - of potential vessel regions in the orthogonal CT slices and uses a convolutional neural network (CNN) to identify the true vessel nodes. The nodes are gradually linked together to generate an approximate vascular skeleton. The CNN classifier yields a precision of 0.81 and recall of 0.83 for the medium size vessels and produces a qualitatively evaluated enhanced representation of vascular skeletons.
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| 3. |
- Lidayová, Kristína, et al.
(author)
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Coverage segmentation of 3D thin structures
- 2015
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In: Image Processing Theory, Tools and Applications (IPTA), 2015 International Conference on. - Piscataway, NJ : IEEE conference proceedings. - 9781479986361 ; , s. 23-28
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Conference paper (peer-reviewed)abstract
- We present a coverage segmentation method for extracting thin structures in three-dimensional images. The proposed method is an improved extension of our coverage segmentation method for 2D thin structures. We suggest implementation that enables low memory consumption and processing time, and by that applicability of the method on real CTA data. The method needs a reliable crisp segmentation as an input and uses information from linear unmixing and the crisp segmentation to create a high-resolution crisp reconstruction of the object, which can then be used as a final result, or down-sampled to a coverage segmentation at the starting image resolution. Performed quantitative and qualitative analysis confirm excellent performance of the proposed method, both on synthetic and on real data, in particular in terms of robustness to noise.
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| 4. |
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| 5. |
- Lidayová, Kristína, 1987-
(author)
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Fast Methods for Vascular Segmentation Based on Approximate Skeleton Detection
- 2017
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Doctoral thesis (other academic/artistic)abstract
- Modern medical imaging techniques have revolutionized health care over the last decades, providing clinicians with high-resolution 3D images of the inside of the patient's body without the need for invasive procedures. Detailed images of the vascular anatomy can be captured by angiography, providing a valuable source of information when deciding whether a vascular intervention is needed, for planning treatment, and for analyzing the success of therapy. However, increasing level of detail in the images, together with a wide availability of imaging devices, lead to an urgent need for automated techniques for image segmentation and analysis in order to assist the clinicians in performing a fast and accurate examination.To reduce the need for user interaction and increase the speed of vascular segmentation, we propose a fast and fully automatic vascular skeleton extraction algorithm. This algorithm first analyzes the volume's intensity histogram in order to automatically adapt the internal parameters to each patient and then it produces an approximate skeleton of the patient's vasculature. The skeleton can serve as a seed region for subsequent surface extraction algorithms. Further improvements of the skeleton extraction algorithm include the expansion to detect the skeleton of diseased arteries and the design of a convolutional neural network classifier that reduces false positive detections of vascular cross-sections. In addition to the complete skeleton extraction algorithm, the thesis presents a segmentation algorithm based on modified onion-kernel region growing. It initiates the growing from the previously extracted skeleton and provides a rapid binary segmentation of tubular structures. To provide the possibility of extracting precise measurements from this segmentation we introduce a method for obtaining a segmentation with subpixel precision out of the binary segmentation and the original image. This method is especially suited for thin and elongated structures, such as vessels, since it does not shrink the long protrusions. The method supports both 2D and 3D image data.The methods were validated on real computed tomography datasets and are primarily intended for applications in vascular segmentation, however, they are robust enough to work with other anatomical tree structures after adequate parameter adjustment, which was demonstrated on an airway-tree segmentation.
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| 6. |
- Lidayová, Kristína, et al.
(author)
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Fast vascular skeleton extraction algorithm
- 2016
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In: Pattern Recognition Letters. - : Elsevier. - 0167-8655 .- 1872-7344. ; 76, s. 67-75
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Journal article (peer-reviewed)abstract
- Vascular diseases are a common cause of death, particularly in developed countries. Computerized image analysis tools play a potentially important role in diagnosing and quantifying vascular pathologies. Given the size and complexity of modern angiographic data acquisition, fast, automatic and accurate vascular segmentation is a challenging task.In this paper we introduce a fully automatic high-speed vascular skeleton extraction algorithm that is intended as a first step in a complete vascular tree segmentation program. The method takes a 3D unprocessed Computed Tomography Angiography (CTA) scan as input and produces a graph in which the nodes are centrally located artery voxels and the edges represent connections between them. The algorithm works in two passes where the first pass is designed to extract the skeleton of large arteries and the second pass focuses on smaller vascular structures. Each pass consists of three main steps. The first step sets proper parameters automatically using Gaussian curve fitting. In the second step different filters are applied to detect voxels - nodes - that are part of arteries. In the last step the nodes are connected in order to obtain a continuous centerline tree for the entire vasculature. Structures found, that do not belong to the arteries, are removed in a final anatomy-based analysis. The proposed method is computationally efficient with an average execution time of 29s and has been tested on a set of CTA scans of the lower limbs achieving an average overlap rate of 97% and an average detection rate of 71%.
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| 7. |
- Lidayová, Kristína, et al.
(author)
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Improved centerline tree detection of diseased peripheral arteries with a cascading algorithm for vascular segmentation
- 2017
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In: Journal of Medical Imaging. - : SPIE - International Society for Optical Engineering. - 2329-4302 .- 2329-4310. ; 4:2
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Journal article (peer-reviewed)abstract
- Vascular segmentation plays an important role in the assessment of peripheral arterial disease. The segmentation is very challenging especially for arteries with severe stenosis or complete occlusion. We present a cascading algorithm for vascular centerline tree detection specializing in detecting centerlines in diseased peripheral arteries. It takes a three-dimensional computed tomography angiography (CTA) volume and returns a vascular centerline tree, which can be used for accelerating and facilitating the vascular segmentation. The algorithm consists of four levels, two of which detect healthy arteries of varying sizes and two that specialize in different types of vascular pathology: severe calcification and occlusion. We perform four main steps at each level: appropriate parameters for each level are selected automatically, a set of centrally located voxels is detected, these voxels are connected together based on the connection criteria, and the resulting centerline tree is corrected from spurious branches. The proposed method was tested on 25 CTA scans of the lower limbs, achieving an average overlap rate of 89% and an average detection rate of 82%. The average execution time using four CPU cores was 70 s, and the technique was successful also in detecting very distal artery branches, e. g., in the foot.
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| 8. |
- Majtner, Tomáš, et al.
(author)
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Improving skin lesion segmentation in dermoscopic images by thin artefacts removal methods
- 2016
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In: Proceedings Of The 2016 6th European Workshop On Visual Information Processing (EUVIP). - 9781509027811
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Conference paper (peer-reviewed)abstract
- In dermoscopic images, various thin artefacts naturally appear, most usually in the form of hairs. While trying to find the border of the skin lesion, these artefacts affect the lesion segmentation methods and also the subsequent classification.Currently, there is a lot of research focus in this area and various methods are presented both for skin lesion segmentation and thin artefacts removal. In this paper, we investigate into three different thin artefacts removal methods and compare their results using two different skin lesion segmentation methods. The segmentation results are compared with groundtruth segmentation. In addition, we introduce our novel artefacts removal method, which combined with the ExpectationMaximization image segmentation outperforms all the tested methods.
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| 9. |
- Sintorn, Ida-Maria, 1976-, et al.
(author)
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Facilitating Ultrastructural Pathology through Automated Imaging and Analysis
- 2019
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In: Journal of Pathology Informatics. - : Elsevier. - 2229-5089 .- 2153-3539. ; 10:1, s. 38-39
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Journal article (other academic/artistic)abstract
- Transmission electron microscopy (TEM) is an important diagnostic tool for analyzing human tissue at the nm scale. It is the only option, or gold standard, for diagnosing several disorders e.g. cilia and renal diseases, rare cancers etc. However, conventional TEM microscopes are highly manual, technically complex and a special environment is required to house the bulky and sensitive machines. Interpretation of information is subjective, time consuming, and relies on a high level of expertise which, unfortunately, is rare for this specialty within pathology. Here, we present methods and results from an ongoing project with the goal to develop a smart and easy to use platform for ultrastructural pathologic diagnoses. The platform is based on the recently developed MiniTEM instrument, a highly automated table-top TEM. In the project we develop image analysis methods for guided as well as fully automated search and analysis of structures of interest. In addition we enrich MiniTEM with an integrated database for convenient image handling and traceability. These points are identified by user representatives as crucial for creating a cost-effective diagnostic platform. We will show strategies and results for using image analysis and machine learning for automated search for objects/regions of interest at low magnification as well as combining multiple object instances acquired at high magnification to enhance nm details necessary for correct diagnosis. This will be exemplified for diagnosing primary cilia dyskinesia and renal disorders. The automation in imaging and analysis within the platform is a big step towards digital ultrapathology.
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| 10. |
- Västberg, Amanda, et al.
(author)
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Investigating thermally induced aggregation of Somatropin- new insights using orthogonal techniques
- 2023
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In: International Journal of Pharmaceutics. - : Elsevier B.V.. - 0378-5173 .- 1873-3476. ; 637
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Journal article (peer-reviewed)abstract
- Three orthogonal techniques were used to provide new insights into thermally induced aggregation of the therapeutic protein Somatropin at pH 5.8 and 7.0. The techniques were Dynamic Light Scattering (DLS), Asymmetric Flow-Field Flow-Fractionation (AF4), and the TEM-based analysis system MiniTEM™. In addition, Differential Scanning Calorimetry (DSC) was used to study the thermal unfolding and stability. DSC and DLS were used to explain the initial aggregation process and aggregation rate at the two pH values. The results suggest that less electrostatic stabilization seems to be the main reason for the faster initial aggregation at pH 5.8, i.e., closer to the isoelectric point of Somatropin. AF4 and MiniTEM were used to investigate the aggregation pathway further. Combining the results allowed us to demonstrate Somatropin's thermal aggregation pathway at pH 7.0. The growth of the aggregates appears to follow two steps. Smaller elongated aggregates are formed in the first step, possibly initiated by partly unfolded species. In the second step, occurring during longer heating, the smaller aggregates assemble into larger aggregates with more complex structures. © 2023 The Author(s)
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