Cellbow:a robust customizable cell segmentation program  

作　　者：Huixia Ren Mengdi Zhao Bo Liu Ruixiao Yao Qi liu Zhipeng Ren Zirui Wu Zongmao Gao Xiaojing Yang Chao Tang 

机构地区：[1]Center for Quantitative Biology and Peking-Tsinghua Center for Life Sciences,Academy for Advaneed Interdisciplinary Studies,Peking University,Beijing 100871,China

出　　处：《Quantitative Biology》2020年第3期245-255,共11页定量生物学（英文版）

基　　金：This work was supported by the Ministry of Science and Technology of China(2015CB910300);the National Key Research and Development Program of China(2018YFA0900700);the National Natural Science Foundation of China(NSFC31700733).Part of the analysis was performed on the High Performance Computing Platform of the Center for Life Science.

摘　　要：Backgrounds Time-lapse live cell imaging of a growing cell population is routine in many biological investigations.A major challenge in imaging analysis is accurate segmentation,a process to define the boundaries of cells based on raw image data.Current segmentation methods relying on single boundary features have problems in robustness when dealing with inhomogeneous foci which invariably happens in cell population imaging.Methods:Combined with a multi-layer training set strategy,we developed a neural-network-based algorithm—Cellbow.Results'Cellbow can achieve accurate and robust segmentation of cells in broad and general settings.It can also facilitate long-term tracking of cell growth and division.To facilitate the application of Cellbow,we provide a website on which one can online test the software,as well as an I mage J plugin for the user to visualize the performance before software installation.Conclusions Cellbow is customizable and generalizable.It is broadly applicable to segmenting fluorescent images of diverse cell types with no further training needed.For bright-field images,only a small set of sample images of the specific cell type from the user may be needed for training.

关 键 词：deep neural network cell segmentation fluorescent cell imaging bright-field cell imaging lineage tracking 

分 类 号：Q2[生物学—细胞生物学]