Phindr3D

Type
Workflow
Author
Mergenthaler, Philipp; orcid.org/0000-0002-9753-6711
Hariharan, Santosh; orcid.org/0000-0002-0941-0044
Pemberton, James M.; orcid.org/0000-0001-8386-1081
Lourenco, Corey; orcid.org/0000-0001-9075-7661
Penn, Linda Z.; orcid.org/0000-0001-8133-5459
Andrews, David W.; orcid.org/0000-0002-9266-7157
Requires
MATLAB
Execution Platform
Windows
Implementation Type
library
standalone
Programming Language
Matlab
is compatible with
MATLAB
Supported image dimension
3D
multi-channel
Interaction Level
Semi-automated
License/Openness
Free and open source
License
GNU General Public License Version 3
Description

Phindr3D is a comprehensive shallow-learning framework for automated quantitative phenotyping of three-dimensional (3D) high content screening image data using unsupervised data-driven voxel-based feature learning, which enables computationally facile classification, clustering and data visualization.

Please see our GitHub page and the original publication for details.

has function
Analysis
Image analysis
Image classification
Image feature detection
Neuron image analysis
Classification
Clustering
Image processing
Pattern recognition
Visualisation
has topic
Bioimage informatics
High content screening
Machine learning
Statistics
Entry Curator
pmergenthaler
Post date
05/26/2021 - 21:54
Last modified
05/26/2021 - 22:30
Download Page
Phindr3D on GitHub
Documentation Link
https://github.com/DWALab/Phindr3D
attribution instructions
Please cite the original manuscript if you find Phindr3D useful for your work:
Mergenthaler P, Hariharan S, Pemberton JM, Lourenco C, Penn LZ, Andrews DW (2021) Rapid 3D phenotypic analysis of neurons and organoids using data-driven cell segmentation-free machine learning. PLoS Comput Biol 17(2): e1008630. (https://doi.org/10.1371/journal.pcbi.1008630).
Reference Publication Link
Phindr3D original publication
Backlinks