Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.
Ignacio Arganda-Carreras,Ignacio Arganda-Carreras,Verena Kaynig,Curtis Rueden,Kevin W. Eliceiri,Johannes Schindelin,Albert Cardona,H. Sebastian Seung +7 more
TLDR
The Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically, is introduced.Abstract:
Summary State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This process is time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leverages a limited number of manual annotations in order to train a classifier and segment the remaining data automatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers. Availability and implementation TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation . Contact ignacio.arganda@ehu.eus. Supplementary information Supplementary data are available at Bioinformatics online.read more
Citations
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ImageJ2: ImageJ for the next generation of scientific image data
Curtis Rueden,Johannes Schindelin,Johannes Schindelin,Mark Hiner,Barry E. DeZonia,Alison E. Walter,Alison E. Walter,Ellen T. Arena,Ellen T. Arena,Kevin W. Eliceiri,Kevin W. Eliceiri +10 more
TL;DR: ImageJ2 as mentioned in this paper is the next generation of ImageJ, which provides a host of new functionality and separates concerns, fully decoupling the data model from the user interface.
Posted Content
ImageJ2: ImageJ for the next generation of scientific image data
Curtis Rueden,Johannes Schindelin,Johannes Schindelin,Mark Hiner,Barry E. DeZonia,Alison E. Walter,Alison E. Walter,Ellen T. Arena,Ellen T. Arena,Kevin W. Eliceiri,Kevin W. Eliceiri +10 more
TL;DR: The entire ImageJ codebase was rewrote, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements.
Journal ArticleDOI
ilastik: interactive machine learning for (bio)image analysis.
Stuart Berg,Dominik Kutra,Thorben Kroeger,Christoph N. Straehle,Bernhard X. Kausler,Carsten Haubold,Martin Schiegg,Janez Ales,Thorsten Beier,Markus Rudy,Kemal Eren,Jaime I Cervantes,Buote Xu,Fynn Beuttenmueller,Adrian Wolny,Chong Zhang,Ullrich Koethe,Fred A. Hamprecht,Anna Kreshuk +18 more
TL;DR: Ilastik as mentioned in this paper is an easy-to-use interactive tool that brings machine-learning-based (bio)image analysis to end users without substantial computational expertise, which contains pre-defined workflows for image segmentation, object classification, counting and tracking.
Journal ArticleDOI
U-Net: deep learning for cell counting, detection, and morphometry
Thorsten Falk,Dominic Mai,Robert Bensch,Özgün Çiçek,Ahmed Abdulkadir,Ahmed Abdulkadir,Yassine Marrakchi,Anton Böhm,Jan Deubner,Zoe Jäckel,Katharina Seiwald,Alexander Dovzhenko,Olaf Tietz,Cristina Dal Bosco,Sean Walsh,Deniz Saltukoglu,Tuan Leng Tay,Marco Prinz,Klaus Palme,Matias Simons,Ilka Diester,Thomas Brox,Olaf Ronneberger +22 more
TL;DR: An ImageJ plugin is presented that enables non-machine-learning experts to analyze their data with U-Net on either a local computer or a remote server/cloud service.
Journal ArticleDOI
Resolving the fibrotic niche of human liver cirrhosis at single cell level
Prakash Ramachandran,Ross Dobie,John R. Wilson-Kanamori,Elena Dora,Beth E. P. Henderson,N T Luu,N T Luu,Jordan R. Portman,Kylie P. Matchett,M Brice,John A. Marwick,Richard S Taylor,Mirjana Efremova,Roser Vento-Tormo,Neil O. Carragher,Timothy J. Kendall,Jonathan A. Fallowfield,Ewen M Harrison,David R. Mole,David R. Mole,Stephen J. Wigmore,Stephen J. Wigmore,Philip N. Newsome,Philip N. Newsome,Christopher J. Weston,Christopher J. Weston,John P. Iredale,Frank Tacke,Jeffrey W. Pollard,Jeffrey W. Pollard,Chris P. Ponting,John C. Marioni,John C. Marioni,John C. Marioni,Sarah A. Teichmann,Sarah A. Teichmann,Sarah A. Teichmann,Neil C. Henderson +37 more
TL;DR: Analysis of transcriptomes of more than 100,000 single human cells yields molecular definitions for non-parenchymal cell types that are found in healthy and cirrhotic human liver, and identifies markers for scar-associated macrophages and endothelial cells.
References
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Fiji: an open-source platform for biological-image analysis
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