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10.1186/s12859-017-1604-1 http://scihub22266oqcxt.onion/10.1186/s12859-017-1604-1 C5364575!5364575!28335722     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28335722&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28335722.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Bioinformatics 2017 ; 18 (�): � Nephropedia Template TP {{tp|p=28335722|t=2017. Generalizing cell segmentation and quantification |pdf=|usr=}}{{28335722}} gab.com Text Generalizing cell segmentation and quantification JO: BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28335722 #FOAvip Background: In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work. Results: To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods. Conclusions: The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users. Twit Text FOAVip Generalizing cell segmentation and quantification ????BMC Bioinformatics http://www.kidney.de/pget.php?&tw=1&pmid=28335722 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=28335722 #FOAvip English Wikipedia <ref>{{Cite pmid|28335722}}</ref> Generalizing cell segmentation and quantification #MMPMID28335722 Wang Z; Li H BMC Bioinformatics 2017[]; 18 (�): � PMID28335722show ga Background: In recent years, the microscopy technology for imaging cells has developed greatly and rapidly. The accompanying requirements for automatic segmentation and quantification of the imaged cells are becoming more and more. After studied widely in both scientific research and industrial applications for many decades, cell segmentation has achieved great progress, especially in segmenting some specific types of cells, e.g. muscle cells. However, it lacks a framework to address the cell segmentation problems generally. On the contrary, different segmentation methods were proposed to address the different types of cells, which makes the research work divergent. In addition, most of the popular segmentation and quantification tools usually require a great part of manual work. Results: To make the cell segmentation work more convergent, we propose a framework that is able to segment different kinds of cells automatically and robustly in this paper. This framework evolves the previously proposed method in segmenting the muscle cells and generalizes it to be suitable for segmenting and quantifying a variety of cell images by adding more union cases. Compared to the previous methods, the segmentation and quantification accuracy of the proposed framework is also improved by three novel procedures: (1) a simplified calibration method is proposed and added for the threshold selection process; (2) a noise blob filter is proposed to get rid of the noise blobs. (3) a boundary smoothing filter is proposed to reduce the false seeds produced by the iterative erosion. As it turned out, the quantification accuracy of the proposed framework increases from 93.4 to 96.8% compared to the previous method. In addition, the accuracy of the proposed framework is also better in quantifying the muscle cells than two available state-of-the-art methods. Conclusions: The proposed framework is able to automatically segment and quantify more types of cells than state-of-the-art methods. Electronic supplementary material: The online version of this article (doi:10.1186/s12859-017-1604-1) contains supplementary material, which is available to authorized users. �Generalizing cell segmentation and quantification (epmc).pdf DeepDyve Pubget Overpricing