Automated phenotype recognition for zebrafish embryo based in vivo high throughput toxicity screening of engineered nano-materials.
Automated phenotype recognition for zebrafish embryo based in vivo high throughput toxicity screening of engineered nano-materials.
复制标题
基于斑马鱼胚胎的自动表型识别基于体内的高吞吐量毒性筛选工程纳米材料的筛选。
DOI：
10.1371/journal.pone.0035014
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Cohen Y
中科院分区：
文献类型：
--
作者：
Liu R;Lin S;Rallo R;Zhao Y;Damoiseaux R;Xia T;Lin S;Nel A;Cohen Y
开发了一种表型识别模型，用于工程化纳米材料（eNMs）毒性的高通量筛选（HTS），其使用斑马鱼胚胎发育响应，根据自动捕获的图像并且在没有人工操作斑马鱼定位的情况下，通过三种基本表型（即，孵化，未孵化，和死亡）。识别模型由一组提供图像颜色和纹理信息的矢量描述子构成。最佳性能的模型，达到了三个图像描述符（颜色直方图，代表性的颜色，和颜色布局）确定为最合适的从一个初始池的六个描述符。该模型在10倍交叉验证中的平均识别准确率为97.40±0.95%，在低质量斑马鱼图像的压力测试中的平均识别准确率为93.75%。目前的工作表明，可以开发出具有准确识别能力的表型模型，适用于基于斑马鱼的HTS检测。虽然本方法仅成功地证明了三种基本的斑马鱼胚胎表型，但它可以很容易地适应更微妙的表型。
A phenotype recognition model was developed for high throughput screening (HTS) of engineered Nano-Materials (eNMs) toxicity using zebrafish embryo developmental response classified, from automatically captured images and without manual manipulation of zebrafish positioning, by three basic phenotypes (i.e., hatched, unhatched, and dead). The recognition model was built with a set of vectorial descriptors providing image color and texture information. The best performing model was attained with three image descriptors (color histogram, representative color, and color layout) identified as most suitable from an initial pool of six descriptors. This model had an average recognition accuracy of 97.40±0.95% in a 10-fold cross-validation and 93.75% in a stress test of low quality zebrafish images. The present work has shown that a phenotyping model can be developed with accurate recognition ability suitable for zebrafish-based HTS assays. Although the present methodology was successfully demonstrated for only three basic zebrafish embryonic phenotypes, it can be readily adapted to incorporate more subtle phenotypes.
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影响因子：
14.8
作者：
Blackburn, Jessica S.;Liu, Sali;Raimondi, Aubrey R.;Ignatius, Myron S.;Salthouse, Christopher D.;Langenau, David M.
通讯作者：
Langenau, David M.
影响因子：
4.1
作者：
Cheng, Jinping;Flahaut, Emmanuel;Cheng, Shuk Han
通讯作者：
Cheng, Shuk Han
影响因子：
46.9
作者：
Colvin, VL
通讯作者：
Colvin, VL
影响因子：
17.1
作者：
Lee, Kerry J.;Nallathamby, Prakash D.;Xu, Xiao-Hong Nancy
通讯作者：
Xu, Xiao-Hong Nancy
影响因子：
14.8
作者：
Burns, CG;Milan, DJ;Fishman, MC
通讯作者：
Fishman, MC