Icon

NucleiClassificationTraining_​OMICS2020

Nuclei Classification Trainig

This workflow reads images from a high throughput experiment acquired with an Olympus ScanR Microscope. A subset of the image data is used to train a classifier, which is then applied to the whole data setA classifier is trained on exemplary images and applied to the whole dataset.


NucleiClassification* example data can be found under the link in the description.* in the List Files Node specify the directory you want to work with.* select images with the two row filters in order to train a classifier. At the moment, the first row filter selects a certain subposition, the second a specific row.* open the Labeling Editor and define a training set by creating new classes and clicking on the according objects in the image. Refer to the help of the node for details.* run the rest of the workflow to classify the whole dataset.* Configure the Excel Writer to save your data accordingly. de.NBI Acknowledgement:If you like this workfllow and want to acknowledge it in your work, please do so! This workflow was composed by Manuel Gunkel (manuel.gunkel@bioquant.uni-heidelberg.de) as partof the German Network for Bioinformatics Infrastructure (HD.HuB as part of de.NBI; grant 031A537C).Please consider visiting https://www.denbi.de for more information and interesting tools and services!Please consider also giving us feedback via the Surveymonkey link in the description, or visit https://www.surveymonkey.de/r/denbi-service?sc=hd-hub&tool=knime-cell to do so. It's really really short just three questions, remarks and comments are optional.Many Thanks!! Copy Image Processing and Feature Extraction from the training branch above downto the processing branch and reconnect them as shown below, in order to have thesame processing steps! (delete the nodes below beforehand or afterwards!) || || \/ \/ files for trainingNode 866Node 879Node 881Node 884write object featuresNode 889Node 898Node 901Node 903Node 904Node 906Node 912Node 913Node 919microscope datadapiwrite grouped dataNode 926Node 928Node 937Node 952Node 953microscope datadapi + cy3Node 980Node 984Node 986Node 987Node 988Node 989Image Processing List Files Normalization Object Counting Metadata Handling Random ForestLearner Plate HeatmapViewer RowID Z-Primes (PC x NC) Excel Writer (XLS)(deprecated) Plate Info Feature Extraction FilenameParserScanR Row Splitter Image Reader(Table) ImageJ Macro Image SegmentFeatures Labeling Properties FilenameParserScanR InteractiveLabeling Editor Conditional BoxPlot (local) Excel Writer (XLS) Random ForestPredictor List Files Excel Writer (XLS)(deprecated) Joiner R Plot Excel Reader (XLS) Sorter Domain Calculator Feature Extraction List Files FilenameParserScanR Row Sampling Image Processing Concatenate Joiner FilenameParserScanR Image Properties MorphologicalLabeling Operations Labeling Arithmetic Joiner NucleiClassification* example data can be found under the link in the description.* in the List Files Node specify the directory you want to work with.* select images with the two row filters in order to train a classifier. At the moment, the first row filter selects a certain subposition, the second a specific row.* open the Labeling Editor and define a training set by creating new classes and clicking on the according objects in the image. Refer to the help of the node for details.* run the rest of the workflow to classify the whole dataset.* Configure the Excel Writer to save your data accordingly. de.NBI Acknowledgement:If you like this workfllow and want to acknowledge it in your work, please do so! This workflow was composed by Manuel Gunkel (manuel.gunkel@bioquant.uni-heidelberg.de) as partof the German Network for Bioinformatics Infrastructure (HD.HuB as part of de.NBI; grant 031A537C).Please consider visiting https://www.denbi.de for more information and interesting tools and services!Please consider also giving us feedback via the Surveymonkey link in the description, or visit https://www.surveymonkey.de/r/denbi-service?sc=hd-hub&tool=knime-cell to do so. It's really really short just three questions, remarks and comments are optional.Many Thanks!! Copy Image Processing and Feature Extraction from the training branch above downto the processing branch and reconnect them as shown below, in order to have thesame processing steps! (delete the nodes below beforehand or afterwards!) || || \/ \/ files for trainingNode 866Node 879Node 881Node 884write object featuresNode 889Node 898Node 901Node 903Node 904Node 906Node 912Node 913Node 919microscope datadapiwrite grouped dataNode 926Node 928Node 937Node 952Node 953microscope datadapi + cy3Node 980Node 984Node 986Node 987Node 988Node 989Image Processing List Files Normalization Object Counting Metadata Handling Random ForestLearner Plate HeatmapViewer RowID Z-Primes (PC x NC) Excel Writer (XLS)(deprecated) Plate Info Feature Extraction FilenameParserScanR Row Splitter Image Reader(Table) ImageJ Macro Image SegmentFeatures Labeling Properties FilenameParserScanR InteractiveLabeling Editor Conditional BoxPlot (local) Excel Writer (XLS) Random ForestPredictor List Files Excel Writer (XLS)(deprecated) Joiner R Plot Excel Reader (XLS) Sorter Domain Calculator Feature Extraction List Files FilenameParserScanR Row Sampling Image Processing Concatenate Joiner FilenameParserScanR Image Properties MorphologicalLabeling Operations Labeling Arithmetic Joiner

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: manu
Created at: 2019-05-14
On NodePit since: 2020-09-25
Last update: 2024-11-17
Created with KNIME version: v4.1.4
Tags: HCSimage segmentationclassificationphenotypes

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!