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Wyszukujesz frazę "Dziekiewicz, Miroslaw" wg kryterium: Autor

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    Wyświetlanie 1-15 z 15
    Tytuł:
    Morphological evaluation of the iliac and femoral arteries; possibilities and perspectives
    Autorzy:
    Dziekiewicz, Mirosław
    Markiewicz, Tomasz
    Kozłowski, Wojciech
    Maruszyński, Marek
    Powiązania:
    https://bibliotekanauki.pl/articles/1395696.pdf
    Data publikacji:
    2014-01-01
    Wydawca:
    Index Copernicus International
    Tematy:
    atherosclerosis
    femoral artery
    iliac artery
    endovascular intervention
    computed tomography
    image analysis
    Opis:
    The study presented an approach to the morphometric image of atherosclerotic lesions of the final segment of the abdominal aorta, femoral and iliac arteries, considering possible endovascular intervention. The evaluation of these arteries is very important, because they are often used as a point of access for endovascular procedures performed on the peripheral arteries, or within the thoracic and abdominal aorta and its branches, as well as coronary arteries. The aim of the study was to determine morphometric measurements describing the atherosclerotic lesions, including the methodology of their surgical interpretation. Material and methods. The study group comprised 128 tomograms of patients qualified for surgery. An algorithm based on the mathematical morphology was designed to track the vessels, starting from the division of the common femoral artery, and ending at the bifurcation of the abdominal aorta. We proposed a set of numerical measurements of the observed arterial changes. Results and conclusions. We analysed 128 tomograms with a 94.5% efficiency, and with the assessment accuracy of the degree of lumen reduction (MAE- 1.5%). We observed much higher measurement values of local tortuosity of the atherosclerotic arteries (0.3 - 1 radians), as compared to their anatomical course in a healthy subject (0 - 0.2 radians). The presented method can be a very accurate and useful tool in the numerical analysis of the lumen distribution of the arteries and atherosclerosis, dedicated to surgeons elaborating management strategies.
    Źródło:
    Polish Journal of Surgery; 2014, 86, 1; 1-6
    0032-373X
    2299-2847
    Pojawia się w:
    Polish Journal of Surgery
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-feature ensemble system in the renal tumour classification task
    Autorzy:
    Osowska-Kurczab, Aleksandra Maria
    Markiewicz, Tomasz
    Dziekiewicz, Miroslaw
    Lorent, Malgorzata
    Powiązania:
    https://bibliotekanauki.pl/articles/2128157.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    medical imaging
    renal cell carcinoma
    convolutional neural networks
    textural features
    support vector machine
    computer vision
    deep learning
    technika deep learning
    obrazowanie medyczne
    rak nerkowokomórkowy
    konwolucyjne sieci neuronowe
    cechy tekstury
    maszyna wektorów nośnych
    wizja komputerowa
    głęboka nauka
    Opis:
    Recently, the analysis of medical imaging is gaining substantial research interest, due to advancements in the computer vision field. Automation of medical image analysis can significantly improve the diagnosis process and lead to better prioritization of patients waiting for medical consultation. This research is dedicated to building a multi-feature ensemble model which associates two independent methods of image description: textural features and deep learning. Different algorithms of classification were applied to single-phase computed tomography images containing 8 subtypes of renal neoplastic lesions. The final ensemble includes a textural description combined with a support vector machine and various configurations of Convolutional Neural Networks. Results of experimental tests have proved that such a model can achieve 93.6% of weighted F1-score (tested in 10-fold cross validation mode). Improvement of performance of the best individual predictor totalled 3.5 percentage points.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136749, 1--8
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multi-feature ensemble system in the renal tumour classification task
    Autorzy:
    Osowska-Kurczab, Aleksandra Maria
    Markiewicz, Tomasz
    Dziekiewicz, Miroslaw
    Lorent, Malgorzata
    Powiązania:
    https://bibliotekanauki.pl/articles/2173572.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    medical imaging
    renal cell carcinoma
    convolutional neural networks
    textural features
    support vector machine
    computer vision
    deep learning
    technika deep learning
    obrazowanie medyczne
    rak nerkowokomórkowy
    konwolucyjne sieci neuronowe
    cechy tekstury
    maszyna wektorów nośnych
    wizja komputerowa
    głęboka nauka
    Opis:
    Recently, the analysis of medical imaging is gaining substantial research interest, due to advancements in the computer vision field. Automation of medical image analysis can significantly improve the diagnosis process and lead to better prioritization of patients waiting for medical consultation. This research is dedicated to building a multi-feature ensemble model which associates two independent methods of image description: textural features and deep learning. Different algorithms of classification were applied to single-phase computed tomography images containing 8 subtypes of renal neoplastic lesions. The final ensemble includes a textural description combined with a support vector machine and various configurations of Convolutional Neural Networks. Results of experimental tests have proved that such a model can achieve 93.6% of weighted F1-score (tested in 10-fold cross validation mode). Improvement of performance of the best individual predictor totalled 3.5 percentage points.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136749
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-15 z 15

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