AUTOMATED QUANTITATIVE ANALYSIS OF MICROGLIA IN BRIGHT-FIELD IMAGES OF ZEBRAFISH

被引：0
|
作者：
Geurts, Samuel N. [1 ,2 ,3 ]
Oosterhof, Nynke [4 ,5 ]
Kuil, Laura E. [4 ]
van der Linde, Hernia C. [4 ]
van Ham, Tjakko J. [4 ]
Meijering, Erik [2 ,3 ,6 ,7 ]
机构：
[1] Delft Univ Technol, Fac Appl Sci, Dept Imaging Phys, Delft, Netherlands
[2] Erasmus MC, Dept Med Informat, Rotterdam, Netherlands
[3] Erasmus MC, Dept Radiol, Rotterdam, Netherlands
[4] Erasmus MC, Dept Clin Genet, Rotterdam, Netherlands
[5] Univ Groningen, Univ Med Ctr Groningen, European Res Inst Biol Ageing, Groningen, Netherlands
[6] Univ New South Wales, Sch Comp Sci & Engn, Sydney, NSW, Australia
[7] Univ New South Wales, Grad Sch Biomed Engn, Sydney, NSW, Australia
来源：
2020 IEEE 17TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI 2020) | 2020年
关键词：
Bioimage analysis; brain segmentation; microglia detection; genetic screening; microscopy; GENOME;
D O I：
10.1109/isbi45749.2020.9098339
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
Microglia are known to play important roles in brain development and homeostasis, yet their molecular regulation is still poorly understood. Identification of microglia regulators is facilitated by genetic screening and studying the phenotypic effects in animal models. Zebrafish are ideal for this, as their external development and transparency allow in vivo imaging by bright-field microscopy in the larval stage. However, manual analysis of the images is very labor intensive. Here we present a computational method to automate the analysis. It merges the optical sections into an all-in-focus image to simplify the subsequent steps of segmenting the brain region and detecting the contained microglia for quantification and downstream statistical testing. Evaluation on a fully annotated data set of 50 zebrafish larvae shows that the method performs close to the human expert.
引用
收藏
页码：522 / 525
页数：4
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