Object Segmentation and Ground Truth in 3D Embryonic Imaging

被引:15
作者
Rajasekaran, Bhavna [1 ,2 ]
Uriu, Koichiro [1 ,2 ,3 ,6 ]
Valentin, Guillaume [1 ,4 ,7 ]
Tinevez, Jean-Yves [1 ,8 ]
Oates, Andrew C. [1 ,4 ,5 ,9 ]
机构
[1] Max Planck Inst Mol Cell Biol & Genet, Dresden, Germany
[2] Max Planck Inst Phys Komplexer Syst, Dresden, Germany
[3] RIKEN, Theoret Biol Lab, Saitama, Japan
[4] MRC Natl Inst Med Res, London, England
[5] UCL, Dept Cell & Dev Biol, London, England
[6] Kanazawa Univ, Grad Sch Nat Sci & Technol, Kanazawa, Ishikawa 9201192, Japan
[7] Genoway, Lyon, France
[8] Inst Pasteur, Imagopole CiTech, Paris, France
[9] Francis Crick Inst, Mill Hill Lab, London, England
来源
PLOS ONE | 2016年 / 11卷 / 06期
基金
英国惠康基金; 欧洲研究理事会; 英国医学研究理事会; 日本学术振兴会;
关键词
CELL BIOLOGY; MICROSCOPY; RECONSTRUCTION; TRACKING; MIGRATION; ACCURATE; NUCLEI;
D O I
10.1371/journal.pone.0150853
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Many questions in developmental biology depend on measuring the position and movement of individual cells within developing embryos. Yet, tools that provide this data are often challenged by high cell density and their accuracy is difficult to measure. Here, we present a three-step procedure to address this problem. Step one is a novel segmentation algorithm based on image derivatives that, in combination with selective post-processing, reliably and automatically segments cell nuclei from images of densely packed tissue. Step two is a quantitative validation using synthetic images to ascertain the efficiency of the algorithm with respect to signal-to-noise ratio and object density. Finally, we propose an original method to generate reliable and experimentally faithful ground truth datasets: Sparse-dense dual-labeled embryo chimeras are used to unambiguously measure segmentation errors within experimental data. Together, the three steps outlined here establish a robust, iterative procedure to fine-tune image analysis algorithms and microscopy settings associated with embryonic 3D image data sets.
引用
收藏
页数:17
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