;
dc1:created "2018-01-28T11:37:30"^^xsd:dateTime ;
dc1:modified "2023-05-02T16:19:55"^^xsd:dateTime ;
dc1:title "2D brain slice region annotation: SliceMap" ;
rdfs:comment """SliceMap
\r
\r
Whole brain tissue slices are commonly used in neurobiological research for analyzing pathological features in an anatomically defined manner. However, since many pathologies are expressed in specific regions of the brain, it is necessary to have an annotation of the regions in the brain slices. Such an annotation can be done by manual delineation, as done most often, or by an automated region annotation tool.
\r
\r
SliceMap is a FIJI/ImageJ plugin for automated brain region annotation of fluorescent brain slices. The plugin uses a reference library of pre-annotated brain slices (the brain region templates) to annotate brain regions of unknown samples. To perform the region annotation, SliceMap registers the reference slices to the sample slice (using elastic registration plugin BUnwarpJ) and uses the resulting image transformations to morph the template regions towards the anatomical brain regions of the sample. The resulting brain regions are saved as FIJI/ImageJ ROI’s (Regions Of Interest) as a single zip-file for each sample slice.
\r
\r
More information can also be found in "SliceMap: an algorithm for automated brain region annotation", Michaël Barbier, Astrid Bottelbergs, Rony Nuydens, Andreas Ebneth, Winnok H De Vos, Bioinformatics, btx658, https://doi.org/10.1093/bioinformatics/btx658
\r
""" .
a ;
nb:hasAuthor "R.A. Tyson, D.B.A. Epstein, K.I. Anderson and T. Bretschneider" ;
nb:hasFunction ,
,
;
nb:hasIllustration "http://biii.eu/sites/default/files/2019-02/boa_plugins.png" ;
nb:hasLocation ,
"Home Page" ;
nb:hasPlatform ,
,
;
nb:hasReferencePublication ,
"Tyson et al (2010) High Resolution Tracking of Cell Membrane Dynamics in Moving Cells: an Electrifying Approach" ;
nb:hasSupportedImageDimension ,
;
nb:hasType ;
nb:openess ;
nb:requires ,
;
dc1:created "2014-12-09T10:02:20"^^xsd:dateTime ;
dc1:modified "2019-02-05T09:55:57"^^xsd:dateTime ;
dc1:title "2D cell tracking and analysis of morphological dynamics" ;
rdfs:comment """The QuimP software from Bretschneider group is deployed as ImageJ plugin and includes model-based cell segmentation, cell outline tracking and quantification of the spatially resolved speed of protrusions and retractions. The algorithm to calculate morphological dynamics is faster compared to other approaches (e.g. [Machacek and Danuser, 2006](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1367294/)). The reference paper describes the workflow for these analyses. \r
""" .
a ;
nb:hasAuthor "Waithe dominic orcid.org/0000-0003-2685-4226" ;
nb:hasDocumentation ,
"Related Wikipedia article." ;
nb:hasFunction ,
;
nb:hasIllustration "http://biii.eu/sites/default/files/2018-10/Screenshot%202018-10-17%20at%2018.24.17.png" ;
nb:hasLocation ,
"IJ1 macro script." ;
nb:hasProgrammingLanguage ;
nb:hasSupportedImageDimension ;
nb:hasTopic ,
;
nb:hasType ;
nb:openess ;
nb:requires ,
;
dc1:created "2018-10-17T17:13:31"^^xsd:dateTime ;
dc1:modified "2021-03-16T21:06:27"^^xsd:dateTime ;
dc1:title "2D Gaussian fitting macro (Fiji/ImageJ) for multiple signals." ;
rdfs:comment """This script includes a rough feature detection and then fine 2D Gaussian algorithm to fit Gaussians within detected regions. This macro is unique because the ImageJ/Fiji curve fitting API only supports 1-D curve. I get around this by linearising the equation. This implementation is for isotropic (spherical) or anistropic (longer in x/y) diagonally covariant Gaussians but not fully covariant Gaussians (anisotropic and rotated).
\r
""" .
a ;
nb:hasAuthor "Juergen Reymann" ;
nb:hasDocumentation ,
"Training material" ;
nb:hasFunction ;
nb:hasIllustration "http://biii.eu/sites/default/files/2018-05/2dspotsKNIME.png" ;
nb:hasLocation ,
"download workflow" ;
nb:hasPlatform ,
,
;
nb:hasProgrammingLanguage ;
nb:hasSupportedImageDimension ;
nb:hasType ;
nb:openess ;
nb:requires ;
dc1:created "2014-12-09T11:35:33"^^xsd:dateTime ;
dc1:modified "2018-05-14T21:26:17"^^xsd:dateTime ;
dc1:title "2D spots counting using KNIME" ;
rdfs:comment """These two KNIME workflow solutions are similar: first one detects nuclei and spots inside the nuclei without taking care of surrounding regions, i.e. mitochondria. The second one provides the full solution including spots in mitochondria.
\r
\r
see section 2.4 for KNIME workflow. Section 2.3 is also available, using Fiji.
\r
\r
Sample image: hela-cells.tif (674k x 3)
\r
""" .
a ;
nb:hasAuthor "Juergen Reymann" ;
nb:hasDocumentation ,
"Chapter 3 2D+time tracking" ;
nb:hasFunction ;
nb:hasIllustration "http://biii.eu/sites/default/files/2018-05/2dcelltracking_KNIME.png" ;
nb:hasLocation ,
"Session4_Tracking" ;
nb:hasPlatform ,
,
;
nb:hasProgrammingLanguage ;
nb:hasSupportedImageDimension ,
;
nb:hasType ;
nb:openess ;
nb:requires ,
;
dc1:created "2014-12-09T11:47:49"^^xsd:dateTime ;
dc1:modified "2018-05-14T21:16:25"^^xsd:dateTime ;
dc1:title "2D tracking using KNIME and Fiji" ;
rdfs:comment """This simple KNIME workflow solution tracks 2D objects/cells in time series. After a few intensity based preprocessing steps, objects/cells are segmented first, then it uses Fiji TrackMate LAP method for the tracking task.
\r
\r
Documentation starts from p23 of the linked PDF.
\r
\r
Example Image: mitocheck_small.tif (2.9M)
\r
""" .
a ;
nb:hasAuthor "Waithe dominic orcid.org/0000-0003-2685-4226" ;
nb:hasDOI ,
"https://zenodo.org/record/1284027#.W8he6xO2nfY" ;
nb:hasFunction ,
,
;
nb:hasIllustration "http://biii.eu/sites/default/files/2018-10/Screenshot%202018-10-18%20at%2009.54.39.png" ;
nb:hasImplementation ;
nb:hasLicense "GNU General Public License v2.0" ;
nb:hasLocation ,
"link to ipython github." ;
nb:hasPlatform ,
,
;
nb:hasProgrammingLanguage ;
nb:hasReferencePublication ,
,
"conference proceedings",
"pdf version" ;
nb:hasSupportedImageDimension ;
nb:hasTopic ,
;
nb:hasType ;
nb:openess ;
nb:requires ,
,
,
,
;
dc1:created "2018-10-18T10:18:22"^^xsd:dateTime ;
dc1:modified "2018-10-18T10:29:30"^^xsd:dateTime ;
dc1:title "3-D Density Kernel Estimation" ;
rdfs:comment """3-D density kernel estimation (DKE-3-D) method, utilises an ensemble of random decision trees for counting objects in 3D images. DKE-3-D avoids the problem of discrete object identification and segmentation, common to many existing 3-D counting techniques, and outperforms other methods when quantification of densely packed and heterogeneous objects is desired.
\r
""" .
a ;
nb:hasAuthor "Alexandre Dufour" ;
nb:hasDocumentation ;
nb:hasFunction ,
;
nb:hasIllustration "http://biii.eu/sites/default/files/2019-02/3DActiveMeshes64.png" ;
nb:hasImplementation ;
nb:hasLocation ;
nb:hasPlatform ,
,
;
nb:hasReferencePublication ,
"3-D Active Meshes: Fast Discrete Deformable Models for Cell Tracking in 3-D Time-Lapse Microscopy" ;
nb:hasSupportedImageDimension ,
;
nb:hasTopic ;
nb:hasType ;
nb:openess ;
nb:requires ,
,
,
,
;
dc1:created "2013-10-11T13:08:41"^^xsd:dateTime ;
dc1:modified "2023-04-29T14:36:45"^^xsd:dateTime ;
dc1:title "3D Active Meshes (Icy)" .
a ;
nb:hasAuthor "Kota Miura" ;
nb:hasFunction ,
;
nb:hasIllustration "http://biii.eu/sites/default/files/2018-05/art-affines.png" ;
nb:hasLocation ,
"affinetransform3Dv3.js" ;
nb:hasPlatform ,
,
;
nb:hasProgrammingLanguage ;
nb:hasSupportedImageDimension ;
nb:hasType ;
nb:openess ;
nb:requires ;
dc1:created "2014-12-09T15:28:50"^^xsd:dateTime ;
dc1:modified "2018-05-30T08:10:34"^^xsd:dateTime ;
dc1:title "3D affine transformation based on paired points" ;
rdfs:comment """Using a text file containing 3D point coordinates as reference pairs, 3D image stack is transformed.
\r
""" .
a ;
nb:hasLocation ;
nb:hasType ;
nb:openess ;
nb:requires ,
;
dc1:created "2013-10-11T13:08:47"^^xsd:dateTime ;
dc1:modified "2017-09-13T10:11:03"^^xsd:dateTime ;
dc1:title "3D Area" .
a ;
nb:hasAuthor "Du CJ, Hawkins PT, Stephens LR, Bretschneider T" ;
nb:hasFunction ;
nb:hasReferencePublication ,
"Du et al (2013) 3D time series analysis of cell shape using Laplacian approaches" ;
nb:hasType ;
nb:openess ;
nb:requires ;
dc1:created "2014-12-08T12:08:18"^^xsd:dateTime ;
dc1:modified "2018-05-31T20:10:08"^^xsd:dateTime ;
dc1:title "3D cell tracking and quantification of shape changes" ;
rdfs:comment """The workflow includes segmentation, tracking and quantifying morphological dynamics of moving cells in 3D. The authors have implemented the workflow in Matlab, but so far there is no download link provided. To apply this workflow, we recommend to contact the authors or to implement the worflow based on the detailed description in the original paper.
\r
""" .
a ;
nb:hasAuthor "Amat Fernando",
"Branson Kristin",
"Keller J Philipp",
"Lemon William",
"McDole Katie",
"Mossing P Daniel",
"Myers W Eugene",
"Wan Yinan" ;
nb:hasFunction ;
nb:hasLocation ,
"Download executables from Git" ;
nb:hasReferencePublication ,
"Article in Nature Methods" ;
nb:hasTopic ,
,
;
nb:hasType ;
nb:hasUsageExample ,
"TGMM docker" ;
nb:openess ;
dc1:created "2019-12-19T13:48:13"^^xsd:dateTime ;
dc1:modified "2020-10-19T15:07:15"^^xsd:dateTime ;
dc1:title "3D cell tracking using Gaussian Mixture Model (TGMM)" ;
rdfs:comment """TGMM is a cell tracking solution for large 3D volume (typically lightsheet).
\r
\r
It detects cell nuclei by fitting gaussians on their fluorescent intensity.
\r
\r
It can run on GPU using CUDA and is called via the command line.
\r
""" .
a ;
nb:hasAuthor "David ROUSSEAU" ;
nb:hasDocumentation ;
nb:hasIllustration "http://biii.eu/sites/default/files/2018-05/flybrain.jpg" ;
nb:hasLocation ;
nb:hasType ;
nb:hasUsageExample ;
nb:openess ;
dc1:created "2018-05-20T17:49:05"^^xsd:dateTime ;
dc1:modified "2018-05-20T17:59:40"^^xsd:dateTime ;
dc1:title "3D confocal noise simulator" ;
rdfs:comment """This Matlab code simulates the noise of the confocal laser scanning microscope depending on the depth in the image stack (serial sections). Using the stack of binary images, it applies different levels of noise in the signal and background parts of the images to simulate confocal images. This is useful for generating "virtual ground truth" images with known values of sample rotation and distortion.
\r
""" .
a ;
nb:hasAuthor "Hazen Babcock",
"Xiaowei Zhuang",
"Yaron M. Sigal" ;
nb:hasComparison ,
"Challenge in SUper Resolution 2016" ;
nb:hasDOI ;
nb:hasFunction ;
nb:hasImplementation ;
nb:hasLocation ;
nb:hasPlatform ,
,
;
nb:hasProgrammingLanguage ;
nb:hasReferencePublication ;
nb:hasSupportedImageDimension ,
;
nb:hasTopic ,
;
nb:hasType ;
nb:openess ;
dc1:created "2018-09-21T11:52:58"^^xsd:dateTime ;
dc1:modified "2023-05-02T10:11:55"^^xsd:dateTime ;
dc1:title "3D-DAOSTORM" ;
rdfs:comment """Stochastic optical reconstruction microscopy (STORM) and related methods achieves sub-diffraction-limit image resolution through sequential activation and localization of individual fluorophores. The analysis of image data from these methods has typically been confined to the sparse activation regime where the density of activated fluorophores is sufficiently low such that there is minimal overlap between the images of adjacent emitters. Recently several methods have been reported for analyzing higher density data, allowing partial overlap between adjacent emitters. However, these methods have so far been limited to two-dimensional imaging, in which the point spread function (PSF) of each emitter is assumed to be identical.
\r
\r
In this work, we present a method to analyze high-density super-resolution data in three dimensions, where the images of individual fluorophores not only overlap, but also have varying PSFs that depend on the z positions of the fluorophores.
\r
\r
\r
""" .
a ;
nb:hasAuthor "A Great Guy" ;
nb:hasType ;
nb:openess ;
dc1:created "2014-12-08T17:29:35"^^xsd:dateTime ;
dc1:modified "2017-09-12T18:04:01"^^xsd:dateTime ;
dc1:title "3D estimation of synaptic vesicle distribution in serial section TEM (ssTEM)" ;
rdfs:comment """An estimate of the shortest distance of vesicles to synaptic cleft is computed in 3D for serial section TEM. Unfortunately the the authors do not provide an implementation.\r
\r
Method:\r
1. Bias correction for inhomogene lighting\r
2. Image registration of TEM sections / stacks\r
3. Detection of vesicles & synaptic cleft (semi-automatic)\r
4. Compute distances in 3D\r
""" .
a ;
nb:hasAuthor "Boudier, Thomas" ;
nb:hasFunction ;
nb:hasImplementation ;
nb:hasLocation ,
"mcib3d Github repository linking to Fill Holes method" ;
nb:hasPlatform ,
,
;
nb:hasProgrammingLanguage ;
nb:hasSupportedImageDimension ;
nb:hasType ;
nb:openess ;
nb:requires ,
;
dc1:created "2019-02-26T15:15:10"^^xsd:dateTime ;
dc1:modified "2019-02-26T15:21:31"^^xsd:dateTime ;
dc1:title "3D Fill holes (mcib3d)" ;
rdfs:comment """Runs fill holes operation on 3D images.
\r
""" .
a ;
nb:hasAuthor "Dimitri Van De Ville",
"Michael Unser",
"Nicolas Chenouard" ;
nb:hasFunction