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10.1002/mp.14609 http://scihub22266oqcxt.onion/10.1002/mp.14609 33225476!7753662!33225476     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 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 Deprecated: Implicit conversion from float 245.2 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 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Med+Phys 2021 ; 48 (4): 1633-1645 Nephropedia Template TP {{tp|p=33225476|t=2021. Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction |pdf=|usr=}}{{33225476}} gab.com Text Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction JO: Med Phys http://www.kidney.de/pget.php?&tw=1&pmid=33225476 #FOAvip OBJECTIVE: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. METHODS: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment. RESULTS: The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6% +/- 10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4% +/- 1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction. CONCLUSIONS: A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction. Twit Text FOAVip Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction ????Med Phys http://www.kidney.de/pget.php?&tw=1&pmid=33225476 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33225476 #FOAvip English Wikipedia <ref>{{Cite pmid|33225476}}</ref> Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction #MMPMID33225476 Shan F; Gao Y; Wang J; Shi W; Shi N; Han M; Xue Z; Shen D; Shi Y Med Phys 2021[Apr]; 48 (4): 1633-1645 PMID33225476show ga OBJECTIVE: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans. METHODS: The DL-based segmentation method employs the "VB-Net" neural network to segment COVID-19 infection regions in CT scans. The developed DL-based segmentation system is trained by CT scans from 249 COVID-19 patients, and further validated by CT scans from other 300 COVID-19 patients. To accelerate the manual delineation of CT scans for training, a human-involved-model-iterations (HIMI) strategy is also adopted to assist radiologists to refine automatic annotation of each training case. To evaluate the performance of the DL-based segmentation system, three metrics, that is, Dice similarity coefficient, the differences of volume, and percentage of infection (POI), are calculated between automatic and manual segmentations on the validation set. Then, a clinical study on severity prediction is reported based on the quantitative infection assessment. RESULTS: The proposed DL-based segmentation system yielded Dice similarity coefficients of 91.6% +/- 10.0% between automatic and manual segmentations, and a mean POI estimation error of 0.3% for the whole lung on the validation dataset. Moreover, compared with the cases with fully manual delineation that often takes hours, the proposed HIMI training strategy can dramatically reduce the delineation time to 4 min after three iterations of model updating. Besides, the best accuracy of severity prediction was 73.4% +/- 1.3% when the mass of infection (MOI) of multiple lung lobes and bronchopulmonary segments were used as features for severity prediction, indicating the potential clinical application of our quantification technique on severity prediction. CONCLUSIONS: A DL-based segmentation system has been developed to automatically segment and quantify infection regions in CT scans of COVID-19 patients. Quantitative evaluation indicated high accuracy in automatic infection delineation and severity prediction. |*Deep Learning[MESH] |*Image Interpretation, Computer-Assisted[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH]Abnormal lung quantification in chest CT images of COVID-19 patients w(epmc).pdf DeepDyve Pubget Overpricing