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10.1109/TMI.2014.2374615 http://scihub22266oqcxt.onion/10.1109/TMI.2014.2374615 C4417425!4417425!25438305     free     free     free Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 209.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 243.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       IEEE+Trans+Med+Imaging 2015 ; 34 (5): 1063-76 Nephropedia Template TP {{tp|p=25438305|t=2015. Graph-Based Airway Tree Reconstruction from Chest CT Scans: Evaluation of Different Features on Five Cohorts |pdf=|usr=}}{{25438305}} gab.com Text Graph-Based Airway Tree Reconstruction from Chest CT Scans: Evaluation of Different Features on Five Cohorts JO: IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=25438305 #FOAvip We present a graph-based framework for airway tree reconstruction from CT scans and evaluate the performance of different feature categories and their combinations on five lung cohorts. The approach consists of two main processing steps. First, potential airway branch and connection candidates are identified and represented by a graph structure with weighted nodes and edges, respectively. Second, an optimization algorithm is utilized for generating an airway detection result by selecting a subset of airway branches and connections based on graph weights derived from image features. The performance of the algorithm with different feature categories and their combinations was assessed on a set of 50 lung CT scans from five different cohorts, including normal and diseased lungs. Results show tradeoffs between feature categories/combinations in terms of correctly (true positive) and incorrectly (false positive) identified airways. Also, the performance of features in dependence of lung cohort was analyzed. Across all cohorts, a good trade-off with high true positive rate (TPR) and low false positive rate (FPR) was achieved by a combination of gray-value, local shape, and structural features. This combination enabled extracting 91.80% of reference airways (TPR) in combination with a low FPR of 1.00%. In addition, this variant was evaluated on the public EXACT?09 test set, and a comparison with other airway detection approaches is provided. One of the main advantages of the presented method is that it is robust against local disturbances/artifacts or other ambiguities that are frequently occurring in lung CT scans. Twit Text FOAVip Graph-Based Airway Tree Reconstruction from Chest CT Scans: Evaluation of Different Features on Five Cohorts ????IEEE Trans Med Imaging http://www.kidney.de/pget.php?&tw=1&pmid=25438305 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25438305 #FOAvip English Wikipedia <ref>{{Cite pmid|25438305}}</ref> Graph-Based Airway Tree Reconstruction from Chest CT Scans: Evaluation of Different Features on Five Cohorts #MMPMID25438305 Bauer C; Eberlein M; Beichel RR IEEE Trans Med Imaging 2015[May]; 34 (5): 1063-76 PMID25438305show ga We present a graph-based framework for airway tree reconstruction from CT scans and evaluate the performance of different feature categories and their combinations on five lung cohorts. The approach consists of two main processing steps. First, potential airway branch and connection candidates are identified and represented by a graph structure with weighted nodes and edges, respectively. Second, an optimization algorithm is utilized for generating an airway detection result by selecting a subset of airway branches and connections based on graph weights derived from image features. The performance of the algorithm with different feature categories and their combinations was assessed on a set of 50 lung CT scans from five different cohorts, including normal and diseased lungs. Results show tradeoffs between feature categories/combinations in terms of correctly (true positive) and incorrectly (false positive) identified airways. Also, the performance of features in dependence of lung cohort was analyzed. Across all cohorts, a good trade-off with high true positive rate (TPR) and low false positive rate (FPR) was achieved by a combination of gray-value, local shape, and structural features. This combination enabled extracting 91.80% of reference airways (TPR) in combination with a low FPR of 1.00%. In addition, this variant was evaluated on the public EXACT?09 test set, and a comparison with other airway detection approaches is provided. One of the main advantages of the presented method is that it is robust against local disturbances/artifacts or other ambiguities that are frequently occurring in lung CT scans. äGraph-Based Airway Tree Reconstruction from Chest CT Scans: Evaluation(epmc).pdf DeepDyve Pubget Overpricing