#BibTeX
#BioImageDbs.bib

##Refs
#http://celltrackingchallenge.net


@InProceedings{Kume2017,
author="Kume, Satoshi and Masuya, Hiroshi and Maeda, Mitsuyo and Suga, Mitsuo and Kataoka, Yosky and Kobayashi, Norio",
editor="Wang, Zhe and Turhan, Anni-Yasmin and Wang, Kewen and Zhang, Xiaowang",
title="Development of Semantic Web-Based Imaging Database for Biological Morphome",
booktitle="Semantic Technology",
year="2017",
publisher="Springer International Publishing",
address="Cham",
pages="277--285",
abstract="We introduce the RIKEN Microstructural Imaging Metadatabase, a semantic web-based imaging database in which image metadata are described using the Resource Description Framework (RDF) and detailed biological properties observed in the images can be represented as Linked Open Data. The metadata are used to develop a large-scale imaging viewer that provides a straightforward graphical user interface to visualise a large microstructural tiling image at the gigabyte level. We applied the database to accumulate comprehensive microstructural imaging data produced by automated scanning electron microscopy. As a result, we have successfully managed vast numbers of images and their metadata, including the interpretation of morphological phenotypes occurring in sub-cellular components and biosamples captured in the images. We also discuss advanced utilisation of morphological imaging data that can be promoted by this database.",
isbn="978-3-319-70682-5",
url="https://link.springer.com/chapter/10.1007/978-3-319-70682-5_19",
doi="10.1007/978-3-319-70682-5_19"
}

@article{Williams2017,
title = "Image Data Resource: A bioimage data integration and publication platform",
abstract = "Access to primary research data is vital for the advancement of science. To extend the data types supported by community repositories, we built a prototype Image Data Resource (IDR). IDR links data from several imaging modalities, including high-content screening, multi-dimensional microscopy and digital pathology, with public genetic or chemical databases and cell and tissue phenotypes expressed using controlled ontologies. Using this integration, IDR facilitates the analysis of gene networks and reveals functional interactions that are inaccessible to individual studies. To enable reanalysis, we also established a computational resource based on Jupyter notebooks that allows remote access to the entire IDR. IDR is also an open-source platform for publishing imaging data. Thus IDR provides an online resource and a software infrastructure that promotes and extends publication and reanalysis of scientific image data.",
author = "Eleanor Williams and Josh Moore and Li, {Simon W.} and Gabriella Rustici and Aleksandra Tarkowska and Anatole Chessel and Simone Leo and B{\'a}lint Antal and Ferguson, {Richard K.} and Ugis Sarkans and Alvis Brazma and {Carazo Salas}, {Rafael E.} and Swedlow, {Jason R.}",
year = "2017",
month = aug,
day = "1",
doi = "10.1038/nmeth.4326",
language = "English",
volume = "14",
pages = "775--781",
journal = "Nature Methods",
issn = "1548-7091",
publisher = "Springer Nature",
number = "8",
}

@article{Kobayashi2018,
	author = {Kobayashi, Norio and Kume, Satoshi and Lenz, Kai and Masuya, Hiroshi},
	title = {RIKEN MetaDatabase: A Database Platform for Health Care and Life Sciences as a Microcosm of Linked Open Data Cloud},
	journal = {International Journal on Semantic Web and Information Systems (IJSWIS)},
	volume = {14},
	number = {1},
	pages = {140--164},
	year = {2018},
	doi = {10.4018/IJSWIS.2018010106}
}

@misc{Chollet2017kerasR,
  title={R Interface to Keras},
  author={Chollet, Fran\c{c}ois and Allaire, JJ and others},
  year={2017},
  publisher={GitHub},
  howpublished={\url{https://github.com/rstudio/keras}},
}

@article{Lucchi2013,
	author = {Lucchi, Aurélien and Li, Yunpeng and Fua, Pascal},
	title = {Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets},
	journal = {2013 IEEE Conference on Computer Vision and Pattern Recognition},
	year = {2013},
	ISBN={978-1-5386-5672-3},
	doi = {10.1109/CVPR.2013.259}
}

@INPROCEEDINGS{Lucchi2013bib,
  author={Lucchi, Aurélien and Li, Yunpeng and Fua, Pascal},
  booktitle={2013 IEEE Conference on Computer Vision and Pattern Recognition}, 
  title={Learning for Structured Prediction Using Approximate Subgradient Descent with Working Sets}, 
  year={2013},
  volume={},
  number={},
  pages={1987-1994},
  doi={10.1109/CVPR.2013.259}}

@article{Lucchi2012,
author = {Lucchi, Aurélien and Smith, Kevin and Achanta, Radhakrishna and Knott, Graham and Fua, Pascal},
title = {Supervoxel-based segmentation of mitochondria in em image stacks with learned shape features},
journal = {IEEE transactions on medical imaging},
volume = {31},
number = {2},
pages = {474--486},
year = {2012},
doi = {10.1109/TMI.2011.2171705}
}

@article{Cardona2010,
doi = {10.1371/journal.pbio.1000502},
author = {Cardona, Albert and Saalfeld, Stephan and Preibisch, Stephan and Schmid, Benjamin and Cheng, Anchi and Pulokas, Jim and Tomancak, Pavel and Hartenstein, Volker},
journal = {PLOS Biology},
publisher = {Public Library of Science},
title = {An Integrated Micro- and Macroarchitectural Analysis of the Drosophila Brain by Computer-Assisted Serial Section Electron Microscopy},
year = {2010},
month = {10},
volume = {8},
url = {https://doi.org/10.1371/journal.pbio.1000502},
pages = {1--17},
abstract = {A new software package allows for dense electron microscopy reconstructions of neuronal networks in the fruit fly brain, and reveals specific differences in microcircuits between insects and vertebrates.},
    number = {10}
}

@article{ArgandaCarreras2015,
AUTHOR={Arganda-Carreras, Ignacio and Turaga, Srinivas C. and Berger, Daniel R. and Cireşan, Dan and Giusti, Alessandro and Gambardella, Luca M. and Schmidhuber, Jürgen and Laptev, Dmitry and Dwivedi, Sarvesh and Buhmann, Joachim M. and Liu, Ting and Seyedhosseini, Mojtaba and Tasdizen, Tolga and Kamentsky, Lee and Burget, Radim and Uher, Vaclav and Tan, Xiao and Sun, Changming and Pham, Tuan D. and Bas, Erhan and Uzunbas, Mustafa G. and Cardona, Albert and Schindelin, Johannes and Seung, H. Sebastian},
TITLE={Crowdsourcing the creation of image segmentation algorithms for connectomics},
JOURNAL={Frontiers in Neuroanatomy},
VOLUME={9},
PAGES={142},
YEAR={2015},
URL={https://www.frontiersin.org/article/10.3389/fnana.2015.00142},
DOI={10.3389/fnana.2015.00142},
ISSN={1662-5129},
ABSTRACT={To stimulate progress in automating the reconstruction of neural circuits, we organized the first international challenge on 2D segmentation of electron microscopic (EM) images of the brain. Participants submitted boundary maps predicted for a test set of images, and were scored based on their agreement with a consensus of human expert annotations. The winning team had no prior experience with EM images, and employed a convolutional network. This “deep learning” approach has since become accepted as a standard for segmentation of EM images. The challenge has continued to accept submissions, and the best so far has resulted from cooperation between two teams. The challenge has probably saturated, as algorithms cannot progress beyond limits set by ambiguities inherent in 2D scoring and the size of the test dataset. Retrospective evaluation of the challenge scoring system reveals that it was not sufficiently robust to variations in the widths of neurite borders. We propose a solution to this problem, which should be useful for a future 3D segmentation challenge.}
}

@article{Morath2013,
  title={Semi-automatic determination of cell surface areas used in systems biology},
  author={Volker Morath and Margret Keuper and Marta Rodriguez-Franco and Sumit Deswal and Gina Fiala and Britta Blumenthal and Daniel Kaschek and Jens Timmer and Gunther Neuhaus and Stephan Ehl and Olaf Ronneberger and Wolfgang Werner A. Schamel},
  journal={Frontiers in Bioscience-Elite},
  volume={5},
  number={2},
  pages={533--545},
  doi = {10.2741/E635},
  year={2013}
}

@article{Maska2014,
    author = {Maška, Martin and Ulman, Vladimír and Svoboda, David and Matula, Pavel and Matula, Petr and Ederra, Cristina and Urbiola, Ainhoa and España, Tomás and Venkatesan, Subramanian and Balak, Deepak M.W. and Karas, Pavel and Bolcková, Tereza and Štreitová, Markéta and Carthel, Craig and Coraluppi, Stefano and Harder, Nathalie and Rohr, Karl and Magnusson, Klas E. G. and Jaldén, Joakim and Blau, Helen M. and Dzyubachyk, Oleh and Křížek, Pavel and Hagen, Guy M. and Pastor-Escuredo, David and Jimenez-Carretero, Daniel and Ledesma-Carbayo, Maria J. and Muñoz-Barrutia, Arrate and Meijering, Erik and Kozubek, Michal and Ortiz-de-Solorzano, Carlos},
    title = "{A benchmark for comparison of cell tracking algorithms}",
    journal = {Bioinformatics},
    volume = {30},
    number = {11},
    pages = {1609-1617},
    year = {2014},
    month = {02},
    abstract = "{Motivation: Automatic tracking of cells in multidimensional time-lapse fluorescence microscopy is an important task in many biomedical applications. A novel framework for objective evaluation of cell tracking algorithms has been established under the auspices of the IEEE International Symposium on Biomedical Imaging 2013 Cell Tracking Challenge. In this article, we present the logistics, datasets, methods and results of the challenge and lay down the principles for future uses of this benchmark.Results: The main contributions of the challenge include the creation of a comprehensive video dataset repository and the definition of objective measures for comparison and ranking of the algorithms. With this benchmark, six algorithms covering a variety of segmentation and tracking paradigms have been compared and ranked based on their performance on both synthetic and real datasets. Given the diversity of the datasets, we do not declare a single winner of the challenge. Instead, we present and discuss the results for each individual dataset separately.Availability and implementation: The challenge Web site (http://www.codesolorzano.com/celltrackingchallenge) provides access to the training and competition datasets, along with the ground truth of the training videos. It also provides access to Windows and Linux executable files of the evaluation software and most of the algorithms that competed in the challenge.Contact:codesolorzano@unav.esSupplementary information:Supplementary data are available at Bioinformatics online.}",
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btu080},
    url = {https://doi.org/10.1093/bioinformatics/btu080},
    eprint = {https://academic.oup.com/bioinformatics/article-pdf/30/11/1609/17143058/btu080.pdf},
}

@article{Ulman2017,
	author = {Vladimír Ulman and Martin Maška and Klas E G Magnusson and Olaf Ronneberger and Carsten Haubold and Nathalie Harder and Pavel Matula and Petr Matula and David Svoboda and Miroslav Radojevic and Ihor Smal and Karl Rohr and Joakim Jaldén and Helen M Blau and Oleh Dzyubachyk and Boudewijn Lelieveldt and Pengdong Xiao and Yuexiang Li and Siu-Yeung Cho and Alexandre C Dufour and Jean-Christophe Olivo-Marin and Constantino C Reyes-Aldasoro and Jose A Solis-Lemus and Robert Bensch and Thomas Brox and Johannes Stegmaier and Ralf Mikut and Steffen Wolf and Fred A Hamprecht and Tiago Esteves and Pedro Quelhas and Ömer Demirel and Lars Malmström and Florian Jug and Pavel Tomancak and Erik Meijering and Arrate Muñoz-Barrutia and Michal Kozubek and Carlos Ortiz-de-Solorzano},
	title = {An objective comparison of cell-tracking algorithms},
	journal = {Nature Methods},
	volume = {14},
	pages = {1141--1152},
	year = {2017},
	doi = {10.1038/nmeth.4473}
}

@article{Bartova2011,
    doi = {10.1371/journal.pone.0027281},
    author = {Bártová, Eva and Šustáčková, Gabriela and Stixová, Lenka and Kozubek, Stanislav and Legartová, Soňa and Foltánková, Veronika},
    journal = {PLOS ONE},
    publisher = {Public Library of Science},
    title = {Recruitment of Oct4 Protein to UV-Damaged Chromatin in Embryonic Stem Cells},
    year = {2011},
    month = {12},
    volume = {6},
    url = {https://doi.org/10.1371/journal.pone.0027281},
    pages = {1-13},
    abstract = {Background Oct4 is a specific marker of embryonic stem cell (ESC) pluripotency. However, little is known regarding how Oct4 responds to DNA damage. Here, we investigated whether Oct4 recognizes damaged chromatin in mouse ESCs stably expressing GFP-Oct4. These experiments should contribute to the knowledge of how ESC genomic integrity is maintained, which is crucial for potential application of human ESCs in regenerative medicine.   Methodology/Principal Findings We used time-lapse confocal microscopy, microirradiation by UV laser (355 nm), induction of DNA lesions by specific agents, and GFP technology to study the Oct4 response to DNA damage. We found that Oct4 accumulates in UV-damaged regions immediately after irradiation in an adenosine triphosphate-dependent manner. Intriguingly, this event was not accompanied by pronounced Nanog and c-MYC recruitment to the UV-damaged sites. The accumulation of Oct4 to UV-damaged chromatin occurred simultaneously with H3K9 deacetylation and H2AX phosphorylation (γH2AX). Moreover, we observed an ESC-specific nuclear distribution of γH2AX after interference to cellular processes, including histone acetylation, transcription, and cell metabolism. Inhibition of histone deacetylases mostly prevented pronounced Oct4 accumulation at UV-irradiated chromatin.   Conclusions/Significance Our studies demonstrate pluripotency-specific events that accompany DNA damage responses. Here, we discuss how ESCs might respond to DNA damage caused by genotoxic injury that might lead to unwanted genomic instability.},
    number = {12}
}

@inproceedings{Kume2016,
  title={Development of an Ontology for an Integrated Image Analysis Platform to enable Global Sharing of Microscopy Imaging Data},
  author={Kume, Satoshi and Masuya, Hiroshi and Kataoka, Yosky and Kobayashi, Norio},
  booktitle={Proceedings of The 15th International Semantic Web Conference (ISWC2016)},
  year={2016}
}


@article{XXX,
	author = {XXX},
	title = {XXX},
	journal = {XXX},
	volume = {XXX},
	number = {XXX},
	pages = {XXX--XXX},
	year = {XXX},
	doi = {XXX}
}


