利用 4D 活细胞成像数据自动检测和跟踪多个细胞。
Automated detection and tracking of many cells by using 4D live-cell imaging data.
机构信息
The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPANThe Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.
The Institute of Statistical Mathematics, Research Organization of Information and Systems, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562, CREST, JST, Kanazawa University, Kakuma, Kanazawa 920-1192, The University of Tokyo, Building 3, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032 Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, The Graduate University for Advanced Studies, 10-3 Midori-cho, Tachikawa, Tokyo 190-8562 and JST, ERATO, 2-2-2 Hikaridai Seika-cho, Soraku-gun, Kyoto-fu, JAPAN.
出版信息
Bioinformatics. 2014 Jun 15;30(12):i43-51. doi: 10.1093/bioinformatics/btu271.
MOTIVATION
Automated fluorescence microscopes produce massive amounts of images observing cells, often in four dimensions of space and time. This study addresses two tasks of time-lapse imaging analyses; detection and tracking of the many imaged cells, and it is especially intended for 4D live-cell imaging of neuronal nuclei of Caenorhabditis elegans. The cells of interest appear as slightly deformed ellipsoidal forms. They are densely distributed, and move rapidly in a series of 3D images. Thus, existing tracking methods often fail because more than one tracker will follow the same target or a tracker transits from one to other of different targets during rapid moves.
RESULTS
The present method begins by performing the kernel density estimation in order to convert each 3D image into a smooth, continuous function. The cell bodies in the image are assumed to lie in the regions near the multiple local maxima of the density function. The tasks of detecting and tracking the cells are then addressed with two hill-climbing algorithms. The positions of the trackers are initialized by applying the cell-detection method to an image in the first frame. The tracking method keeps attacking them to near the local maxima in each subsequent image. To prevent the tracker from following multiple cells, we use a Markov random field (MRF) to model the spatial and temporal covariation of the cells and to maximize the image forces and the MRF-induced constraint on the trackers. The tracking procedure is demonstrated with dynamic 3D images that each contain >100 neurons of C.elegans.
AVAILABILITY
http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014 SUPPLEMENTARY INFORMATION: Supplementary data are available at http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014
动机
自动化荧光显微镜产生大量的图像来观察细胞，通常是在空间和时间的四个维度上。本研究解决了延时成像分析的两个任务；检测和跟踪许多成像的细胞，特别是针对秀丽隐杆线虫神经元核的 4D 活细胞成像。感兴趣的细胞呈现出略微变形的椭圆形。它们密集分布，并在一系列 3D 图像中快速移动。因此，现有的跟踪方法通常会失败，因为一个以上的跟踪器将跟随同一个目标，或者在快速移动过程中，一个跟踪器从一个目标转移到另一个目标。
结果
本方法首先通过进行核密度估计，将每个 3D 图像转换为平滑连续的函数。图像中的细胞体被假定位于密度函数的多个局部最大值附近的区域。然后，使用两个爬山算法来解决检测和跟踪细胞的任务。跟踪器的位置通过将细胞检测方法应用于第一帧中的图像来初始化。跟踪方法在后续的每个图像中都将其保持在接近局部最大值的位置。为了防止跟踪器跟踪多个细胞，我们使用马尔可夫随机场 (MRF) 来对细胞的空间和时间变化进行建模，并最大化图像力和 MRF 对跟踪器的约束。跟踪过程用包含 >100 个秀丽隐杆线虫神经元的动态 3D 图像进行演示。
可用性
http://daweb.ism.ac.jp/yoshidalab/crest/ismb2014
补充信息
Zotero 插件