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10.1088/1361-6560/abc04e http://scihub22266oqcxt.onion/10.1088/1361-6560/abc04e 33045699!ä!33045699 Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33045699&cmd=llinks): Failed to open stream: HTTP request failed! 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Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations |pdf=|usr=}}{{33045699}} gab.com Text Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations JO: Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=33045699 #FOAvip Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised method is a promising way to leverage unlabelled data to improve segmentation performance. Existing methods usually obtain pseudo labels by first training a network with limited labelled images and then inferring unlabelled images. However, these methods may generate obviously inaccurate labels and degrade the subsequent training process. To address these challenges, in this paper, an active contour regularized semi-supervised learning framework was proposed to automatically segment infections with few labelled images. The active contour regularization was realized by the region-scalable fitting (RSF) model which is embedded to the loss function of the network to regularize and refine the pseudo labels of the unlabelled images. We further designed a splitting method to separately optimize the RSF regularization term and the segmentation loss term with iterative convolution-thresholding method and stochastic gradient descent, respectively, which enable fast optimization of each term. Furthermore, we built a statistical atlas to show the infection spatial distribution. Extensive experiments on a small public dataset and a large scale dataset showed that the proposed method outperforms state-of-the-art methods with up to 5% in dice similarity coefficient and normalized surface dice, 10% in relative absolute volume difference and 8 mm in 95% Hausdorff distance. Moreover, we observed that the infections tend to occur at the dorsal subpleural lung and posterior basal segments that are not mentioned in current radiology reports and are meaningful to advance our understanding of COVID-19. Twit Text FOAVip Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations ????Phys Med Biol http://www.kidney.de/pget.php?&tw=1&pmid=33045699 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33045699 #FOAvip English Wikipedia <ref>{{Cite pmid|33045699}}</ref> Active contour regularized semi-supervised learning for COVID-19 CT infection segmentation with limited annotations #MMPMID33045699 Ma J; Nie Z; Wang C; Dong G; Zhu Q; He J; Gui L; Yang X Phys Med Biol 2020[Dec]; 65 (22): 225034 PMID33045699show ga Infection segmentation on chest CT plays an important role in the quantitative analysis of COVID-19. Developing automatic segmentation tools in a short period with limited labelled images has become an urgent need. Pseudo label-based semi-supervised method is a promising way to leverage unlabelled data to improve segmentation performance. Existing methods usually obtain pseudo labels by first training a network with limited labelled images and then inferring unlabelled images. However, these methods may generate obviously inaccurate labels and degrade the subsequent training process. To address these challenges, in this paper, an active contour regularized semi-supervised learning framework was proposed to automatically segment infections with few labelled images. The active contour regularization was realized by the region-scalable fitting (RSF) model which is embedded to the loss function of the network to regularize and refine the pseudo labels of the unlabelled images. We further designed a splitting method to separately optimize the RSF regularization term and the segmentation loss term with iterative convolution-thresholding method and stochastic gradient descent, respectively, which enable fast optimization of each term. Furthermore, we built a statistical atlas to show the infection spatial distribution. Extensive experiments on a small public dataset and a large scale dataset showed that the proposed method outperforms state-of-the-art methods with up to 5% in dice similarity coefficient and normalized surface dice, 10% in relative absolute volume difference and 8 mm in 95% Hausdorff distance. Moreover, we observed that the infections tend to occur at the dorsal subpleural lung and posterior basal segments that are not mentioned in current radiology reports and are meaningful to advance our understanding of COVID-19. |*Supervised Machine Learning[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnostic imaging[MESH] |Humans[MESH] |Image Processing, Computer-Assisted/*methods[MESH]Active contour regularized semi-supervised learning for COVID-19 CT in(epmc).pdf DeepDyve Pubget Overpricing