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    Wyświetlanie 1-5 z 5
    Tytuł:
    Monitoring of the Achilles tendon healing process: can artificial intelligence be helpful?
    Autorzy:
    Kapiński, Norbert
    Zieliński, Jakub
    Borucki, Bartosz A.
    Trzciński, Tomasz
    Ciszkowska-Łysoń, Beata
    Zdanowicz, Urszula
    Śmigielski, Robert
    Nowiński, Krzysztof S.
    Powiązania:
    https://bibliotekanauki.pl/articles/306348.pdf
    Data publikacji:
    2019
    Wydawca:
    Politechnika Wrocławska. Oficyna Wydawnicza Politechniki Wrocławskiej
    Tematy:
    rezonans magnetyczny
    obrazowanie medyczne
    sztuczna inteligencja
    tendon traumas
    magnetic resonance
    medical imaging
    artificial intelligence
    deep learning
    Opis:
    The aim of this study was to verify improved, ensemble-based strategy for inferencing with use of our solution for quantitative assessment of tendons and ligaments healing process and to show possible applications of the method. Methods: We chose the problem of the Achilles tendon rupture as an example representing a group of common sport traumas. We derived our dataset from 90 individuals and divided it into two subsets: healthy individuals and patients with complete Achilles tendon ruptures. We computed approx. 160 000 2D axial cross-sections from 3D MRI studies and preprocessed them to create a suitable input for artificial intelligence methods. Finally, we compared different training methods for chosen approaches for quantitative assessment of tendon tissue healing with the use of statistical analysis. Results: We showed improvement in inferencing with use of the ensemble technique that results from achieving comparable accuracy of 99% for our previously published method trained on 500 000 samples and for the new ensemble technique trained on 160 000 samples. We also showed real-life applications of our approach that address several clinical problems: (1) automatic classification of healthy and injured tendons, (2) assessment of the healing process, (3) a pathologic tissue localization. Conclusions: The presented method enables acquiring comparable accuracy with less training samples. The applications of the method presented in the paper as case studies can facilitate evaluation of the healing process and comparing with previous examination of the same patient as well as with other patients. This approach might be probably transferred to other musculoskeletal tissues and joints.
    Źródło:
    Acta of Bioengineering and Biomechanics; 2019, 21, 1; 103-111
    1509-409X
    2450-6303
    Pojawia się w:
    Acta of Bioengineering and Biomechanics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2128156.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136750, 1--7
    0239-7528
    Pojawia się w:
    Bulletin of the Polish Academy of Sciences. Technical Sciences
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Deep learning-based framework for tumour detection and semantic segmentation
    Autorzy:
    Kot, Estera
    Krawczyk, Zuzanna
    Siwek, Krzysztof
    Królicki, Leszek
    Czwarnowski, Piotr
    Powiązania:
    https://bibliotekanauki.pl/articles/2173573.pdf
    Data publikacji:
    2021
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    deep learning
    medical imaging
    tumour detection
    semantic segmentation
    image fusion
    technika deep learning
    głęboka nauka
    obrazowanie medyczne
    wykrywanie guza
    segmentacja semantyczna
    połączenie obrazu
    Opis:
    For brain tumour treatment plans, the diagnoses and predictions made by medical doctors and radiologists are dependent on medical imaging. Obtaining clinically meaningful information from various imaging modalities such as computerized tomography (CT), positron emission tomography (PET) and magnetic resonance (MR) scans are the core methods in software and advanced screening utilized by radiologists. In this paper, a universal and complex framework for two parts of the dose control process – tumours detection and tumours area segmentation from medical images is introduced. The framework formed the implementation of methods to detect glioma tumour from CT and PET scans. Two deep learning pre-trained models: VGG19 and VGG19-BN were investigated and utilized to fuse CT and PET examinations results. Mask R-CNN (region-based convolutional neural network) was used for tumour detection – output of the model is bounding box coordinates for each object in the image – tumour. U-Net was used to perform semantic segmentation – segment malignant cells and tumour area. Transfer learning technique was used to increase the accuracy of models while having a limited collection of the dataset. Data augmentation methods were applied to generate and increase the number of training samples. The implemented framework can be utilized for other use-cases that combine object detection and area segmentation from grayscale and RGB images, especially to shape computer-aided diagnosis (CADx) and computer-aided detection (CADe) systems in the healthcare industry to facilitate and assist doctors and medical care providers.
    Źródło:
    Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136750
    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
    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
      Wyświetlanie 1-5 z 5

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