A deep learning-based algorithm for 2-D cell segmentation in microscopy images.
A deep learning-based algorithm for 2-D cell segmentation in microscopy images.
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DOI：
10.1186/s12859-018-2375-z
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发表时间：
2018-10-03
影响因子：
3
通讯作者：
Rusu M
中科院分区：
文献类型：
--
作者：
Al-Kofahi Y;Zaltsman A;Graves R;Marshall W;Rusu M
Automatic and reliable characterization of cells in cell cultures is key to several applications such as cancer research and drug discovery. Given the recent advances in light microscopy and the need for accurate and high-throughput analysis of cells, automated algorithms have been developed for segmenting and analyzing the cells in microscopy images. Nevertheless, accurate, generic and robust whole-cell segmentation is still a persisting need to precisely quantify its morphological properties, phenotypes and sub-cellular dynamics. We present a single-channel whole cell segmentation algorithm. We use markers that stain the whole cell, but with less staining in the nucleus, and without using a separate nuclear stain. We show the utility of our approach in microscopy images of cell cultures in a wide variety of conditions. Our algorithm uses a deep learning approach to learn and predict locations of the cells and their nuclei, and combines that with thresholding and watershed-based segmentation. We trained and validated our approach using different sets of images, containing cells stained with various markers and imaged at different magnifications. Our approach achieved a 86% similarity to ground truth segmentation when identifying and separating cells. The proposed algorithm is able to automatically segment cells from single channel images using a variety of markers and magnifications. The online version of this article (10.1186/s12859-018-2375-z) contains supplementary material, which is available to authorized users.
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影响因子：
12.3
作者：
Carpenter AE;Jones TR;Lamprecht MR;Clarke C;Kang IH;Friman O;Guertin DA;Chang JH;Lindquist RA;Moffat J;Golland P;Sabatini DM
通讯作者：
Sabatini DM
DOI：
10.1534/g3.116.033654
发表时间：
2017-05-05
期刊：
G3 (Bethesda, Md.)
影响因子：
--
作者：
Pärnamaa T;Parts L
通讯作者：
Parts L
影响因子：
10.9
作者：
Su, Hang;Yin, Zhaozheng;Kanade, Takeo
通讯作者：
Kanade, Takeo
影响因子：
14.9
作者：
Meijering, Erik
通讯作者：
Meijering, Erik
DOI：
10.1093/bioinformatics/btw252
发表时间：
2016-06-15
期刊：
Bioinformatics (Oxford, England)
影响因子：
--
作者：
Kraus OZ;Ba JL;Frey BJ
通讯作者：
Frey BJ