Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.7507/1001-5515.202008032 http://scihub22266oqcxt.onion/10.7507/1001-5515.202008032 33913299!9927687!33913299     free     free     free Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538 Warning: Undefined variable $yww in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 538       Sheng+Wu+Yi+Xue+Gong+Cheng+Xue+Za+Zhi 2021 ; 38 (2): 379-386 Nephropedia Template TP {{tp|p=33913299|t=2021. Research progress in lung parenchyma segmentation based on computed tomography |pdf=|usr=}}{{33913299}} gab.com Text Research progress in lung parenchyma segmentation based on computed tomography JO: Sheng Wu Yi Xue Gong Cheng Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=33913299 #FOAvip Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields. Twit Text FOAVip Research progress in lung parenchyma segmentation based on computed tomography ????Sheng Wu Yi Xue Gong Cheng Xue Za Zhi http://www.kidney.de/pget.php?&tw=1&pmid=33913299 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33913299 #FOAvip English Wikipedia <ref>{{Cite pmid|33913299}}</ref> Research progress in lung parenchyma segmentation based on computed tomography #MMPMID33913299 Xiao H; Ran Z; Huang J; Ren H; Liu C; Zhang B; Zhang B; Dang J Sheng Wu Yi Xue Gong Cheng Xue Za Zhi 2021[Apr]; 38 (2): 379-386 PMID33913299show ga Lung diseases such as lung cancer and COVID-19 seriously endanger human health and life safety, so early screening and diagnosis are particularly important. computed tomography (CT) technology is one of the important ways to screen lung diseases, among which lung parenchyma segmentation based on CT images is the key step in screening lung diseases, and high-quality lung parenchyma segmentation can effectively improve the level of early diagnosis and treatment of lung diseases. Automatic, fast and accurate segmentation of lung parenchyma based on CT images can effectively compensate for the shortcomings of low efficiency and strong subjectivity of manual segmentation, and has become one of the research hotspots in this field. In this paper, the research progress in lung parenchyma segmentation is reviewed based on the related literatures published at domestic and abroad in recent years. The traditional machine learning methods and deep learning methods are compared and analyzed, and the research progress of improving the network structure of deep learning model is emphatically introduced. Some unsolved problems in lung parenchyma segmentation were discussed, and the development prospect was prospected, providing reference for researchers in related fields. |*COVID-19[MESH] |Humans[MESH] |Lung/diagnostic imaging[MESH] |Machine Learning[MESH] |SARS-CoV-2[MESH] Research progress in lung parenchyma segmentation based on computed t(epmc).pdf DeepDyve Pubget Overpricing