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10.1016/j.ejmp.2021.06.001 http://scihub22266oqcxt.onion/10.1016/j.ejmp.2021.06.001 34139383!9188767!34139383     free     free     free Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 235.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Phys+Med 2021 ; 87 (ä): 115-122 Nephropedia Template TP {{tp|p=34139383|t=2021. Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis |pdf=|usr=}}{{34139383}} gab.com Text Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis JO: Phys Med http://www.kidney.de/pget.php?&tw=1&pmid=34139383 #FOAvip PURPOSE: To assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images. METHODS: Four different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (DeltaQMs) were calculated between RS and other segmentations. RESULTS: Highest QS and lower DeltaQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median DeltaQMs values ranging from 10HU and 158HU between different algorithms. CONCLUSIONS: None of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses. Twit Text FOAVip Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis ????Phys Med http://www.kidney.de/pget.php?&tw=1&pmid=34139383 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34139383 #FOAvip English Wikipedia <ref>{{Cite pmid|34139383}}</ref> Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis #MMPMID34139383 Berta L; Rizzetto F; De Mattia C; Lizio D; Felisi M; Colombo PE; Carrazza S; Gelmini S; Bianchi L; Artioli D; Travaglini F; Vanzulli A; Torresin A Phys Med 2021[Jul]; 87 (ä): 115-122 PMID34139383show ga PURPOSE: To assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images. METHODS: Four different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (DeltaQMs) were calculated between RS and other segmentations. RESULTS: Highest QS and lower DeltaQMs values were associated to the CNN algorithm. However, only 45% CNN segmentations were judged to need no or only minimal corrections, and in only 17 cases (31%), automatic segmentations provided RS without manual corrections. Median values of the DI for the four algorithms ranged from 0.993 to 0.904. Significant differences for all QMs calculated between automatic segmentations and RS were found both when data were pooled together and stratified according to QS, indicating a relationship between qualitative and quantitative measurements. The most unstable QM was the histogram 90th percentile, with median DeltaQMs values ranging from 10HU and 158HU between different algorithms. CONCLUSIONS: None of tested algorithms provided fully reliable segmentation. Segmentation accuracy impacts differently on different quantitative metrics, and each of them should be individually evaluated according to the purpose of subsequent analyses. |*COVID-19[MESH] |Algorithms[MESH] |Humans[MESH] |Image Processing, Computer-Assisted[MESH] |Lung[MESH] |Neural Networks, Computer[MESH] |SARS-CoV-2[MESH]Automatic lung segmentation in COVID-19 patients: Impact on quantitati(epmc).pdf DeepDyve Pubget Overpricing