Semi-automatic organelle detection on transmission electron microscopic images

被引：5

作者：

Higaki, Takumi ^{[1
]}

Kutsuna, Natsumaro ^{[1
,2
]}

Akita, Kae ^{[1
]}

Sato, Mayuko ^{[3
]}

Sawaki, Fumie ^{[4
]}

Kobayashi, Megumi ^{[4
]}

Nagata, Noriko ^{[4
]}

Toyooka, Kiminori ^{[3
]}

Hasezawa, Seiichiro ^{[1
]}

机构：

[1] Univ Tokyo, Dept Integrated Biosci, Grad Sch Frontier Sci, Kashiwa, Chiba 2778562, Japan

[2] LPixel Inc, Res & Dev Div, Bunkyo Ku, Tokyo 1500002, Japan

[3] RIKEN, Ctr Sustainable Resource Sci, Tsurumi Ku, Yokohama, Kanagawa 2300045, Japan

[4] Japan Womens Univ, Fac Sci, Bunkyo Ku, Tokyo 1128681, Japan

来源：

SCIENTIFIC REPORTS | 2015年 / 5卷

基金：

日本学术振兴会;

关键词：

RANDOM SUBWINDOWS; MICROGRAPHS; FRAMEWORK;

D O I：

10.1038/srep07794

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：

Recent advances in the acquisition of large-scale datasets of transmission electron microscope images have allowed researchers to determine the number and the distribution of subcellular ultrastructures at both the cellular level and the tissue level. For this purpose, it would be very useful to have a computer-assisted system to detect the structures of interest, such as organelles. Using our original image recognition framework CARTA (Clustering-Aided Rapid Training Agent), combined with procedures to highlight and enlarge regions of interest on the image, we have developed a successful method for the semi-automatic detection of plant organelles including mitochondria, amyloplasts, chloroplasts, etioplasts, and Golgi stacks in transmission electron microscope images. Our proposed semi-automatic detection system will be helpful for labelling organelles in the interpretation and/or quantitative analysis of large-scale electron microscope imaging data.

引用

页数：9

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