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Oriol Rodriguez-Leon.A.Carol;H.Tizon; Eduard Fernandez-Nofrerias; Josefina Mauri; Vicente del Valle; Debora Gil; Aura Hernandez-Sabate; Petia Radeva |
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Model estadístic-determinístic per la segmentació de l adventicia en imatges d ecografía intracoronaria |
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2005 |
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Rev Societat Catalana Cardiologia |
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5 |
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41 |
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IAM @ iam @ RCT2005 |
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1637 |
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Ole Larsen; Petia Radeva; Enric Marti |
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Bounds on the optimal elasticity parameters for a snake |
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1995 |
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Image Analysis and Processing |
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37-42 |
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This paper develops a formalism by which an estimate for the upper and lower bounds for the elasticity parameters for a snake can be obtained. Objects different in size and shape give rise to different bounds. The bounds can be obtained based on an analysis of the shape of the object of interest. Experiments on synthetic images show a good correlation between the estimated behaviour of the snake and the one actually observed. Experiments on real X-ray images show that the parameters for optimal segmentation lie within the estimated bounds. |
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MILAB;IAM |
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IAM @ iam @ LRM1995a |
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1559 |
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Sonia Baeza; Debora Gil; I.Garcia Olive; M.Salcedo; J.Deportos; Carles Sanchez; Guillermo Torres; G.Moragas; Antoni Rosell |
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A novel intelligent radiomic analysis of perfusion SPECT/CT images to optimize pulmonary embolism diagnosis in COVID-19 patients |
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2022 |
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EJNMMI Physics |
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EJNMMI-PHYS |
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9 |
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1, Article 84 |
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1-17 |
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Background: COVID-19 infection, especially in cases with pneumonia, is associated with a high rate of pulmonary embolism (PE). In patients with contraindications for CT pulmonary angiography (CTPA) or non-diagnostic CTPA, perfusion single-photon emission computed tomography/computed tomography (Q-SPECT/CT) is a diagnostic alternative. The goal of this study is to develop a radiomic diagnostic system to detect PE based only on the analysis of Q-SPECT/CT scans.
Methods: This radiomic diagnostic system is based on a local analysis of Q-SPECT/CT volumes that includes both CT and Q-SPECT values for each volume point. We present a combined approach that uses radiomic features extracted from each scan as input into a fully connected classifcation neural network that optimizes a weighted crossentropy loss trained to discriminate between three diferent types of image patterns (pixel sample level): healthy lungs (control group), PE and pneumonia. Four types of models using diferent confguration of parameters were tested.
Results: The proposed radiomic diagnostic system was trained on 20 patients (4,927 sets of samples of three types of image patterns) and validated in a group of 39 patients (4,410 sets of samples of three types of image patterns). In the training group, COVID-19 infection corresponded to 45% of the cases and 51.28% in the test group. In the test group, the best model for determining diferent types of image patterns with PE presented a sensitivity, specifcity, positive predictive value and negative predictive value of 75.1%, 98.2%, 88.9% and 95.4%, respectively. The best model for detecting
pneumonia presented a sensitivity, specifcity, positive predictive value and negative predictive value of 94.1%, 93.6%, 85.2% and 97.6%, respectively. The area under the curve (AUC) was 0.92 for PE and 0.91 for pneumonia. When the results obtained at the pixel sample level are aggregated into regions of interest, the sensitivity of the PE increases to 85%, and all metrics improve for pneumonia.
Conclusion: This radiomic diagnostic system was able to identify the diferent lung imaging patterns and is a frst step toward a comprehensive intelligent radiomic system to optimize the diagnosis of PE by Q-SPECT/CT. |
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5 dec 2022 |
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Admin @ si @ BGG2022 |
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3759 |
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Josep Llados; Enric Marti |
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Graph-edit algorithms for hand-drawn graphical document recognition and their automatic introduction |
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1999 |
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Machine Graphics & Vision journal, special issue on Graph transformation |
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IAM @ iam @ LIM1999c |
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1569 |
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Oriol Rodriguez-Leon; Josefina Mauri;Eduard Fernandez-Nofrerias; C.Garcia; R.Villuendas; Vicente del Valle; Debora Gil;Petia Radeva |
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Reconstruction of a spatio-temporal model of the intima layer from intravascular ultrasound sequences |
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2003 |
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European Heart Journal |
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ESC Congress |
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IAM @ iam @ RMF2003c |
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1641 |
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