000 | 04061nam a22005535i 4500 | ||
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001 | 978-3-030-76394-7 | ||
003 | DE-He213 | ||
005 | 20250526092116.0 | ||
007 | cr nn 008mamaa | ||
008 | 220928s2022 sz | s |||| 0|eng d | ||
020 |
_a9783030763947 _9978-3-030-76394-7 |
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024 | 7 |
_a10.1007/978-3-030-76394-7 _2doi |
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050 | 4 | _aQH573-671 | |
072 | 7 |
_aPSF _2bicssc |
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072 | 7 |
_aSCI017000 _2bisacsh |
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072 | 7 |
_aPSF _2thema |
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082 | 0 | 4 |
_a571.6 _223 |
245 | 1 | 0 |
_aBioimage Data Analysis Workflows ‒ Advanced Components and Methods _h[electronic resource] / _cedited by Kota Miura, Nataša Sladoje. |
250 | _a1st ed. 2022. | ||
264 | 1 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2022. |
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300 |
_aX, 212 p. 265 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aLearning Materials in Biosciences, _x2509-6133 |
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505 | 0 | _aIntroduction -- Batch Processing Methods in ImageJ -- Python: Data Handling, Analysis and Plotting -- Building a Bioimage Analysis Workflow Using Deep Learning -- GPU-Accelerating ImageJ Macro Image Processing Workflows Using CLIJ -- How to Do the Deconstruction of Bioimage Analysis Workflows: A Case Study with SurfCut -- i.2.i. with the (Fruit) Fly: Quantifying Position Effect Variegation in Drosophila Melanogaster -- A MATLAB Pipeline for Spatiotemporal Quantification of Monolayer Cell Migration. | |
506 | 0 | _aOpen Access | |
520 | _aThis open access textbook aims at providing detailed explanations on how to design and construct image analysis workflows to successfully conduct bioimage analysis. Addressing the main challenges in image data analysis, where acquisition by powerful imaging devices results in very large amounts of collected image data, the book discusses techniques relying on batch and GPU programming, as well as on powerful deep learning-based algorithms. In addition, downstream data processing techniques are introduced, such as Python libraries for data organization, plotting, and visualizations. Finally, by studying the way individual unique ideas are implemented in the workflows, readers are carefully guided through how the parameters driving biological systems are revealed by analyzing image data. These studies include segmentation of plant tissue epidermis, analysis of the spatial pattern of the eye development in fruit flies, and the analysis of collective cell migration dynamics. The presented content extends the Bioimage Data Analysis Workflows textbook (Miura, Sladoje, 2020), published in this same series, with new contributions and advanced material, while preserving the well-appreciated pedagogical approach adopted and promoted during the training schools for bioimage analysis organized within NEUBIAS – the Network of European Bioimage Analysts. This textbook is intended for advanced students in various fields of the life sciences and biomedicine, as well as staff scientists and faculty members who conduct regular quantitative analyses of microscopy images. | ||
650 | 0 | _aCytology. | |
650 | 0 | _aBioinformatics. | |
650 | 0 | _aImaging systems in biology. | |
650 | 1 | 4 | _aCell Biology. |
650 | 2 | 4 | _aComputational and Systems Biology. |
650 | 2 | 4 | _aBiological Imaging. |
700 | 1 |
_aMiura, Kota. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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700 | 1 |
_aSladoje, Nataša. _eeditor. _4edt _4http://id.loc.gov/vocabulary/relators/edt |
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710 | 2 | _aSpringerLink (Online service) | |
773 | 0 | _tSpringer Nature eBook | |
776 | 0 | 8 |
_iPrinted edition: _z9783030763930 |
776 | 0 | 8 |
_iPrinted edition: _z9783030763954 |
830 | 0 |
_aLearning Materials in Biosciences, _x2509-6133 |
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856 | 4 | 0 | _uhttps://doi.org/10.1007/978-3-030-76394-7 |
912 | _aZDB-2-SBL | ||
912 | _aZDB-2-SXB | ||
912 | _aZDB-2-SOB | ||
999 |
_c87 _d87 |