The Experts below are selected from a list of 30147 Experts worldwide ranked by ideXlab platform
Reza Piri - One of the best experts on this subject based on the ideXlab platform.
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global cardiac atherosclerotic burden assessed by artificial intelligence based versus Manual Segmentation in 18f sodium fluoride pet ct scans head to head comparison
Journal of Nuclear Cardiology, 2021Co-Authors: Reza Piri, Lars Edenbrandt, Mans Larsson, Olof Enqvist, Sofie Skovrup, Kasper Iversen, Babak SabouryAbstract:Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac Segmentation in positron emission tomography/computed tomography (PET/CT) scans with Manual Segmentation to assess global cardiac atherosclerosis burden. Methods: A trained convolutional neural network (CNN) was used for cardiac Segmentation in 18F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with Manual Segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of Manual Segmentation was examined by re-Segmentation in 25 randomly selected scans. Results: Mean (± SD) values with Manual vs. CNN-based Segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P =.21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P =.26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P =.34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P =.62). Limits of agreement were − 89.42 to 74.2, − 0.02 to 0.02, − 1.52 to 1.32, and − 68.02 to 63.21, respectively. Manual Segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at Manual re-Segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators. Conclusion: The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the Manually obtained ones. This AI-based Segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome Manual Segmentation of the heart.
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aortic wall Segmentation in 18f sodium fluoride pet ct scans head to head comparison of artificial intelligence based versus Manual Segmentation
Journal of Nuclear Cardiology, 2021Co-Authors: Reza Piri, Lars Edenbrandt, Mans Larsson, Olof Enqvist, Amalie Horstmann Noddeskoufink, Oke Gerke, Poul Flemming HoilundcarlsenAbstract:We aimed to establish and test an automated AI-based method for rapid Segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans. For Segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on 18F-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by Manual Segmentation. The following derived parameters were compared using Bland–Altman Limits of Agreement: segmented volume, and maximal, mean, and total standardized uptake values (SUVmax, SUVmean, SUVtotal). The repeatability of the Manual method was examined in 25 randomly selected scans. CNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding Manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual Segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat Manual Segmentation was 6%. The automated CNN-based approach was much faster and provided parameters that were about 15% lower than the Manually obtained values, except for SUVmean values, which were comparable. AI-based Segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome Manual Segmentation.
Poul Flemming Hoilundcarlsen - One of the best experts on this subject based on the ideXlab platform.
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aortic wall Segmentation in 18f sodium fluoride pet ct scans head to head comparison of artificial intelligence based versus Manual Segmentation
Journal of Nuclear Cardiology, 2021Co-Authors: Reza Piri, Lars Edenbrandt, Mans Larsson, Olof Enqvist, Amalie Horstmann Noddeskoufink, Oke Gerke, Poul Flemming HoilundcarlsenAbstract:We aimed to establish and test an automated AI-based method for rapid Segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans. For Segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on 18F-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by Manual Segmentation. The following derived parameters were compared using Bland–Altman Limits of Agreement: segmented volume, and maximal, mean, and total standardized uptake values (SUVmax, SUVmean, SUVtotal). The repeatability of the Manual method was examined in 25 randomly selected scans. CNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding Manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual Segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat Manual Segmentation was 6%. The automated CNN-based approach was much faster and provided parameters that were about 15% lower than the Manually obtained values, except for SUVmean values, which were comparable. AI-based Segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome Manual Segmentation.
Babak Saboury - One of the best experts on this subject based on the ideXlab platform.
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global cardiac atherosclerotic burden assessed by artificial intelligence based versus Manual Segmentation in 18f sodium fluoride pet ct scans head to head comparison
Journal of Nuclear Cardiology, 2021Co-Authors: Reza Piri, Lars Edenbrandt, Mans Larsson, Olof Enqvist, Sofie Skovrup, Kasper Iversen, Babak SabouryAbstract:Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac Segmentation in positron emission tomography/computed tomography (PET/CT) scans with Manual Segmentation to assess global cardiac atherosclerosis burden. Methods: A trained convolutional neural network (CNN) was used for cardiac Segmentation in 18F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with Manual Segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of Manual Segmentation was examined by re-Segmentation in 25 randomly selected scans. Results: Mean (± SD) values with Manual vs. CNN-based Segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P =.21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P =.26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P =.34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P =.62). Limits of agreement were − 89.42 to 74.2, − 0.02 to 0.02, − 1.52 to 1.32, and − 68.02 to 63.21, respectively. Manual Segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at Manual re-Segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators. Conclusion: The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the Manually obtained ones. This AI-based Segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome Manual Segmentation of the heart.
Lars Edenbrandt - One of the best experts on this subject based on the ideXlab platform.
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global cardiac atherosclerotic burden assessed by artificial intelligence based versus Manual Segmentation in 18f sodium fluoride pet ct scans head to head comparison
Journal of Nuclear Cardiology, 2021Co-Authors: Reza Piri, Lars Edenbrandt, Mans Larsson, Olof Enqvist, Sofie Skovrup, Kasper Iversen, Babak SabouryAbstract:Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac Segmentation in positron emission tomography/computed tomography (PET/CT) scans with Manual Segmentation to assess global cardiac atherosclerosis burden. Methods: A trained convolutional neural network (CNN) was used for cardiac Segmentation in 18F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with Manual Segmentation. Parameters for segmented volume (Vol) and mean, maximal, and total standardized uptake values (SUVmean, SUVmax, SUVtotal) were analyzed by Bland-Altman Limits of Agreement. Repeatability with AI-based assessment of the same scans is 100%. Repeatability (same conditions, same operator) and reproducibility (same conditions, two different operators) of Manual Segmentation was examined by re-Segmentation in 25 randomly selected scans. Results: Mean (± SD) values with Manual vs. CNN-based Segmentation were Vol 617.65 ± 154.99 mL vs 625.26 ± 153.55 mL (P =.21), SUVmean 0.69 ± 0.15 vs 0.69 ± 0.15 (P =.26), SUVmax 2.68 ± 0.86 vs 2.77 ± 1.05 (P =.34), and SUVtotal 425.51 ± 138.93 vs 427.91 ± 132.68 (P =.62). Limits of agreement were − 89.42 to 74.2, − 0.02 to 0.02, − 1.52 to 1.32, and − 68.02 to 63.21, respectively. Manual Segmentation lasted typically 30 minutes vs about one minute with the CNN-based approach. The maximal deviation at Manual re-Segmentation was for the four parameters 0% to 0.5% with the same and 0% to 1% with different operators. Conclusion: The CNN-based method was faster and provided values for Vol, SUVmean, SUVmax, and SUVtotal comparable to the Manually obtained ones. This AI-based Segmentation approach appears to offer a more reproducible and much faster substitute for slow and cumbersome Manual Segmentation of the heart.
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aortic wall Segmentation in 18f sodium fluoride pet ct scans head to head comparison of artificial intelligence based versus Manual Segmentation
Journal of Nuclear Cardiology, 2021Co-Authors: Reza Piri, Lars Edenbrandt, Mans Larsson, Olof Enqvist, Amalie Horstmann Noddeskoufink, Oke Gerke, Poul Flemming HoilundcarlsenAbstract:We aimed to establish and test an automated AI-based method for rapid Segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans. For Segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on 18F-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by Manual Segmentation. The following derived parameters were compared using Bland–Altman Limits of Agreement: segmented volume, and maximal, mean, and total standardized uptake values (SUVmax, SUVmean, SUVtotal). The repeatability of the Manual method was examined in 25 randomly selected scans. CNN-derived values for volume, SUVmax, and SUVtotal were all slightly, i.e., 13-17%, lower than the corresponding Manually obtained ones, whereas SUVmean values for the three aortic sections were virtually identical for the two methods. Manual Segmentation lasted typically 1-2 hours per scan compared to about one minute with the CNN-based approach. The maximal deviation at repeat Manual Segmentation was 6%. The automated CNN-based approach was much faster and provided parameters that were about 15% lower than the Manually obtained values, except for SUVmean values, which were comparable. AI-based Segmentation of the aorta already now appears as a trustworthy and fast alternative to slow and cumbersome Manual Segmentation.
Wayne Colizza - One of the best experts on this subject based on the ideXlab platform.
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machine learning derived Segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
Journal of Cardiovascular Magnetic Resonance, 2019Co-Authors: Alex Bratt, Meridith P Pollie, Ashley Beecy, Nathan H Tehrani, Noel C F Codella, Rocio Perezjohnston, Maria Chiara Palumbo, Javid Alakbarli, Wayne Colizza, Ian R DrexlerAbstract:Background Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming Manual Segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow.
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machine learning derived Segmentation of phase velocity encoded cardiovascular magnetic resonance for fully automated aortic flow quantification
Journal of Cardiovascular Magnetic Resonance, 2019Co-Authors: Alex Bratt, Meridith P Pollie, Ashley Beecy, Nathan H Tehrani, Noel C F Codella, Rocio Perezjohnston, Maria Chiara Palumbo, Javid Alakbarli, Jiwon Kim, Wayne ColizzaAbstract:Phase contrast (PC) cardiovascular magnetic resonance (CMR) is widely employed for flow quantification, but analysis typically requires time consuming Manual Segmentation which can require human correction. Advances in machine learning have markedly improved automated processing, but have yet to be applied to PC-CMR. This study tested a novel machine learning model for fully automated analysis of PC-CMR aortic flow. A machine learning model was designed to track aortic valve borders based on neural network approaches. The model was trained in a derivation cohort encompassing 150 patients who underwent clinical PC-CMR then compared to Manual and commercially-available automated Segmentation in a prospective validation cohort. Further validation testing was performed in an external cohort acquired from a different site/CMR vendor. Among 190 coronary artery disease patients prospectively undergoing CMR on commercial scanners (84% 1.5T, 16% 3T), machine learning Segmentation was uniformly successful, requiring no human intervention: Segmentation time was < 0.01 min/case (1.2 min for entire dataset); Manual Segmentation required 3.96 ± 0.36 min/case (12.5 h for entire dataset). Correlations between machine learning and Manual Segmentation-derived flow approached unity (r = 0.99, p < 0.001). Machine learning yielded smaller absolute differences with Manual Segmentation than did commercial automation (1.85 ± 1.80 vs. 3.33 ± 3.18 mL, p < 0.01): Nearly all (98%) of cases differed by ≤5 mL between machine learning and Manual methods. Among patients without advanced mitral regurgitation, machine learning correlated well (r = 0.63, p < 0.001) and yielded small differences with cine-CMR stroke volume (∆ 1.3 ± 17.7 mL, p = 0.36). Among advanced mitral regurgitation patients, machine learning yielded lower stroke volume than did volumetric cine-CMR (∆ 12.6 ± 20.9 mL, p = 0.005), further supporting validity of this method. Among the external validation cohort (n = 80) acquired using a different CMR vendor, the algorithm yielded equivalently small differences (∆ 1.39 ± 1.77 mL, p = 0.4) and high correlations (r = 0.99, p < 0.001) with Manual Segmentation, including similar results in 20 patients with bicuspid or stenotic aortic valve pathology (∆ 1.71 ± 2.25 mL, p = 0.25). Fully automated machine learning PC-CMR Segmentation performs robustly for aortic flow quantification - yielding rapid Segmentation, small differences with Manual Segmentation, and identification of differential forward/left ventricular volumetric stroke volume in context of concomitant mitral regurgitation. Findings support use of machine learning for analysis of large scale CMR datasets.