A machine learning-based method to detect fluorescent spots and an accelerated, parallel implementation of this method  

作　　者：Bei Liu Wanjuan Feng Tao Xu Wei Ji 

机构地区：[1]College of Life Science and Technology, Huazhong University of Science and Technology [2]National Laboratory of Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences

出　　处：《Chinese Science Bulletin》2014年第28期3573-3578,共6页

基　　金：supported by the National Natural Science Foundation of China(31130065,31100615)

摘　　要：Under most microscopes, the fluorescent signals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distribution. Accurate segmentation of these spots will fundamentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the interesting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm AdaBoost, which outperforms threshold-based segmentation,wavelets, and FDA under most situations. We also provide a GPU/multi-core CPU implementation of this algorithm;this implementation accelerates the algorithm approximately 10- and 7-fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. Furthermore, we demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection methods outperform the four other previously reported methods.Under most microscopes, the fluorescent sig- nals emitted from biological structures, such as vesicles, as well as the signals from single molecules will appear as small puncta, which contribute to a Gaussian-like distri- bution. Accurate segmentation of these spots will funda- mentally affect our interpretation of a specific biological progress. Because of the complicated backgrounds in images, many algorithms fail to identify all of the inter- esting signals; the tremendous amount of time required for algorithms to process large datasets can also decrease their utility. Here, we introduce an excellent robust detection method based on the machine learning algorithm Ada- Boost, which outperforms threshold-based segmentation, wavelets, and FDA under most situations. We also provide a GPU/multi-core CPU implementation of this algorithm; this implementation accelerates the algorithm approxi- mately 10- and 7-fold acceleration compared with a single CPU implementation. The great reduction of time should make this method a promising candidate in the processing of large datasets. Furthermore, we demonstrate the use of our algorithm on true fluorescent micrographs, and the results show that machine learning-based detection meth- ods outperform the four other previously reported methods.

关 键 词：机器学习算法 荧光 检测 并行实现 基础 斑点 生物结构 NALS 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]