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

共 50 条

[1] Semi-automatic organelle detection on transmission electron microscopic images
Takumi Higaki
Natsumaro Kutsuna
Kae Akita
Mayuko Sato
Fumie Sawaki
Megumi Kobayashi
Noriko Nagata
Kiminori Toyooka
Seiichiro Hasezawa
Scientific Reports, 5
[2] Semi-automatic background detection in microscopic images
Bevilacqua, Alessandro
Gherardi, Alessandro
Carozza, Ludovico
Piccinini, Filippo
World Academy of Science, Engineering and Technology, 2010, 47 : 275 - 278
[3] PROTOCOL FOR A SEMI-AUTOMATIC ANALYSIS OF ELECTRON-MICROSCOPIC PLATES
CAPRON, R
HAYMANN, P
BIOLOGY OF THE CELL, 1984, 52 (03) : A122 - A122
[4] Semi-Automatic Red Blood Cells Counting in Microscopic Digital Images
Abbas, Naveed
Mohamad, Dzulkifli
Abdullah, Abdul Hanan
JURNAL TEKNOLOGI, 2015, 73 (02):
[5] Semi-automatic Transmission in a Car
Gasparik, M.
Cerny, M.
Solek, P.
Matak, M.
ADVANCES IN MECHANISM DESIGN II, 2017, 44 : 383 - 389
[6] Semi-automatic Detection of Skin Malformations by Analysis of Spectral Images
Rubins, U.
Spigulis, J.
Valeine, L.
Berzina, A.
CLINICAL AND BIOMEDICAL SPECTROSCOPY AND IMAGING III, 2013, 8798
[7] Semi-automatic segmentation for gonioscopic images
Spagnuolo, Giusi
De Giusti, Andrea
INVESTIGATIVE OPHTHALMOLOGY & VISUAL SCIENCE, 2022, 63 (07)
[8] Semi-Automatic Neuron Tracing in Electron Microscope Images: Preliminary Results
Wang, Deli
Liu, Weijie
Zheng, Nenggan
Zhao, Ting
IEEE 2018 INTERNATIONAL CONGRESS ON CYBERMATICS / 2018 IEEE CONFERENCES ON INTERNET OF THINGS, GREEN COMPUTING AND COMMUNICATIONS, CYBER, PHYSICAL AND SOCIAL COMPUTING, SMART DATA, BLOCKCHAIN, COMPUTER AND INFORMATION TECHNOLOGY, 2018, : 733 - 738
[9] Semi-automatic boundary detection for identification of cells in dic microscope images
Young, D
Gray, AJ
SIXTH INTERNATIONAL CONFERENCE ON IMAGE PROCESSING AND ITS APPLICATIONS, VOL 1, 1997, (443): : 346 - 350
[10] ARG: A Semi-automatic system for ROI detection on Renal Scintigraphic images
Aribi, Yassine
Wali, Ali
Hamza, Fatma
Guermazi, Fadhel
Alimi, Adel M.
2014 14TH INTERNATIONAL CONFERENCE ON HYBRID INTELLIGENT SYSTEMS (HIS), 2014, : 303 - 308

← 1 2 3 4 5 →