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10.1155/2015/545809 http://scihub22266oqcxt.onion/10.1155/2015/545809 C4485948!4485948!26170896     free     free     free Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 233.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Math+Methods+Med 2015 ; 2015 (ä): ä Nephropedia Template TP {{tp|p=26170896|t=2015. Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets |pdf=|usr=}}{{26170896}} gab.com Text Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=26170896 #FOAvip An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron. Twit Text FOAVip Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=26170896 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=26170896 #FOAvip English Wikipedia <ref>{{Cite pmid|26170896}}</ref> Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets #MMPMID26170896 Bhikha C; Andreasen A; Christensen EI; Letts RFR; Pantanowitz A; Rubin DM; Thomsen JS; Zhai XY Comput Math Methods Med 2015[]; 2015 (ä): ä PMID26170896show ga An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron. äTowards Automated Three-Dimensional Tracking of Nephrons through Stack(epmc).pdf DeepDyve Pubget Overpricing