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10.1364/BOE.6.001172 http://scihub22266oqcxt.onion/10.1364/BOE.6.001172 C4399658!4399658!25909003     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Biomed+Opt+Express 2015 ; 6 (4): 1172-94 Nephropedia Template TP {{tp|p=25909003|t=2015. Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema |pdf=|usr=}}{{25909003}} gab.com Text Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema JO: Biomed Opt Express http://www.kidney.de/pget.php?&tw=1&pmid=25909003 #FOAvip We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME. Twit Text FOAVip Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema ????Biomed Opt Express http://www.kidney.de/pget.php?&tw=1&pmid=25909003 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=25909003 #FOAvip English Wikipedia <ref>{{Cite pmid|25909003}}</ref> Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema #MMPMID25909003 Chiu SJ; Allingham MJ; Mettu PS; Cousins SW; Izatt JA; Farsiu S Biomed Opt Express 2015[Apr]; 6 (4): 1172-94 PMID25909003show ga We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME. äKernel regression based segmentation of optical coherence tomography i(epmc).pdf DeepDyve Pubget Overpricing