Browsing by Author "Filipović, Nenad (35749660900)"

Localization of lumbar discs in magnetic resonance imaging (MRI) is a challenging task, due to a vast range of shape, size, number, and appearance of discs and vertebrae. Based on a review of the cutting-edge methods, the majority of applied techniques are either semi-automatic, extremely sensitive to change in parameters, or involve further modification of the results. All of the above represents a motivation for implementing deep learning-based approaches for automatic segmentation and classification of disc herniation in MR images. This paper proposes a complete automated process based on deep learning to diagnose disc herniation. The methodology includes several steps starting from segmentation of region of interest (ROI), in this case disc area, bounding box cropping and enhancement of ROI, after which the image is classified based on convolutional neural network (CNN) into adequate classes (healthy, bulge, central, right or left herniation for axial view and healthy, L4/L5, L5/S1 level of herniation in sagittal view). The results show high accuracy of segmentation for both axial view (dice = 0.961, IOU = 0.925) and sagittal view (dice = 0.897, IOU = 0.813) images. After cropping and enhancing the region of interest, accuracy of classification was 0.87 for axial view images and 0.91 for sagittal view images. Comparison with the literature shows that proposed methodology outperforms state-of-the-art results when it comes to multiclassification problems. A fully automated decision support system for disc hernia diagnosis can assist in generating diagnostic findings in a timely manner, while human mistakes caused by cognitive overload and procedure-related errors can be reduced. © 2021 IEEE.

Localization of lumbar discs in magnetic resonance imaging (MRI) is a challenging task, due to a vast range of shape, size, number, and appearance of discs and vertebrae. Based on a review of the cutting-edge methods, the majority of applied techniques are either semi-automatic, extremely sensitive to change in parameters, or involve further modification of the results. All of the above represents a motivation for implementing deep learning-based approaches for automatic segmentation and classification of disc herniation in MR images. This paper proposes a complete automated process based on deep learning to diagnose disc herniation. The methodology includes several steps starting from segmentation of region of interest (ROI), in this case disc area, bounding box cropping and enhancement of ROI, after which the image is classified based on convolutional neural network (CNN) into adequate classes (healthy, bulge, central, right or left herniation for axial view and healthy, L4/L5, L5/S1 level of herniation in sagittal view). The results show high accuracy of segmentation for both axial view (dice = 0.961, IOU = 0.925) and sagittal view (dice = 0.897, IOU = 0.813) images. After cropping and enhancing the region of interest, accuracy of classification was 0.87 for axial view images and 0.91 for sagittal view images. Comparison with the literature shows that proposed methodology outperforms state-of-the-art results when it comes to multiclassification problems. A fully automated decision support system for disc hernia diagnosis can assist in generating diagnostic findings in a timely manner, while human mistakes caused by cognitive overload and procedure-related errors can be reduced. © 2021 IEEE.

Introduction/Objective Finite element analysis (FEA) is mathematical method which can be used for the assessment of biomechanical behavior of dento-alveolar complex. The objectives were to analyze biomechanical behavior changes of teeth and supporting tissues under occlusal load in cases of horizontal and vertical alveolar bone loss, to assess potential impact of tooth displacement and altered stress distribution on further damage, and to evaluate the impact of regenerative periodontal therapy. Methods Three patient-specific three-dimensional finite element (3D FE) models were developed from the acquired cone beam computed tomography, comprising the patient’s upper left canine, first and second premolar, and adjacent bone. Model 1 represented horizontal bone loss. Model 2 included intrabony defect along distal aspect of tooth #24. Model 3 represented situation six months after the regenerative periodontal surgery. Displacement, Von Mises, and principal stresses were evaluated through FEA, under moderate vertical occlusal load. Results FEA demonstrated that in the model with vertical bone loss significant tooth displacement was present, even though the clinically evident tooth mobility was absent. Biomechanical behavior and stress distribution of teeth and surrounding tissues under moderate occlusal load was much more altered in case with vertical bone loss in comparison with horizontal bone loss. Six months following the regenerative therapy, the values of all evaluated parameters were noticeable reduced. Conclusion Regenerative periodontal therapy improved the biomechanical characteristics of the affected teeth and the related periodontal structures. © 2021, Serbia Medical Society. All rights reserved.