@phdthesis{Jenett2007,
  author    = {Jenett, Arnim},
  title     = {The Virtual Insect Brain Protocol : development and application of software for the standardization of neuroanatomy},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-22297},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2007},
  abstract  = {Since the fruit fly Drosophila melanogaster entered the laboratories as a model organism, new genetic, physiological, molecular and behavioral techniques for the functional analysis of the brain rapidly accumulated. Nowadays this concerted assault obtains its main thrust form Gal4 expression patterns that can be visualized and provide the means for manipulating -in unrestrained animals- groups of neurons of the brain. To take advantage of these patterns one needs to know their anatomy. This thesis describes the Virtual Insect Brain (VIB) protocol, a software package for the quantitative assessment, comparison, and presentation of neuroanatomical data. It is based on the 3D-reconstruction and visualization software Amira (Mercury Inc.). Its main part is a standardization procedure which aligns individual 3D images (series of virtual sections obtained by confocal microscopy) to a common coordinate system and computes average intensities for each voxel (volume pixel). The VIB protocol facilitates direct comparison of gene expression patterns and describes their interindividual variability. It provides volumetry of brain regions and helps to characterize the phenotypes of brain structure mutants. Using the VIB protocol does not require any programming skills since all operations are carried out at a (near to) self-explanatory graphical user interface. Although the VIB protocol has been developed for the standardization of Drosophila neuroanatomy, the program structure can be used for the standardization of other 3D structures as well. Standardizing brains and gene expression patterns is a new approach to biological shape and its variability. Using the VIB protocol consequently may help to integrate knowledge on the correlation of form and function of the insect brain. The VIB protocol provides a first set of tools supporting this endeavor in Drosophila. The software is freely available at http://www.neurofly.de.},
  subject      = {Taufliege},
  language  = {en}
}
@article{KarulinKaracsonyZhangetal.2015,
  author    = {Karulin, Alexey Y. and Karacsony, Kinga and Zhang, Wenji and Targoni, Oleg S. and Moldova, Ioana and Dittrich, Marcus and Sundararaman, Srividya and Lehmann, Paul V.},
  title     = {ELISPOTs produced by CD8 and CD4 cells follow Log Normal size distribution permitting objective counting},
  series = {Cells},
  volume    = {4},
  journal   = {Cells},
  number    = {1},
  doi       = {10.3390/cells4010056},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-149648},
  pages     = {56-70},
  year      = {2015},
  abstract  = {Each positive well in ELISPOT assays contains spots of variable sizes that can range from tens of micrometers up to a millimeter in diameter. Therefore, when it comes to counting these spots the decision on setting the lower and the upper spot size thresholds to discriminate between non-specific background noise, spots produced by individual T cells, and spots formed by T cell clusters is critical. If the spot sizes follow a known statistical distribution, precise predictions on minimal and maximal spot sizes, belonging to a given T cell population, can be made. We studied the size distributional properties of IFN-γ, IL-2, IL-4, IL-5 and IL-17 spots elicited in ELISPOT assays with PBMC from 172 healthy donors, upon stimulation with 32 individual viral peptides representing defined HLA Class I-restricted epitopes for CD8 cells, and with protein antigens of CMV and EBV activating CD4 cells. A total of 334 CD8 and 80 CD4 positive T cell responses were analyzed. In 99.7\% of the test cases, spot size distributions followed Log Normal function. These data formally demonstrate that it is possible to establish objective, statistically validated parameters for counting T cell ELISPOTs.},
  language  = {en}
}
@article{AhmedZeeshanDandekar2016,
  author    = {Ahmed, Zeeshan and Zeeshan, Saman and Dandekar, Thomas},
  title     = {Mining biomedical images towards valuable information retrieval in biomedical and life sciences},
  series = {Database - The Journal of Biological Databases and Curation},
  volume    = {2016},
  journal   = {Database - The Journal of Biological Databases and Curation},
  doi       = {10.1093/database/baw118},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-162697},
  pages     = {baw118},
  year      = {2016},
  abstract  = {Biomedical images are helpful sources for the scientists and practitioners in drawing significant hypotheses, exemplifying approaches and describing experimental results in published biomedical literature. In last decades, there has been an enormous increase in the amount of heterogeneous biomedical image production and publication, which results in a need for bioimaging platforms for feature extraction and analysis of text and content in biomedical images to take advantage in implementing effective information retrieval systems. In this review, we summarize technologies related to data mining of figures. We describe and compare the potential of different approaches in terms of their developmental aspects, used methodologies, produced results, achieved accuracies and limitations. Our comparative conclusions include current challenges for bioimaging software with selective image mining, embedded text extraction and processing of complex natural language queries.},
  language  = {en}
}