Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification

By Ignacio Arganda-Carreras, V. Kaynig, Curtis T. Rueden, Kevin W. Eliceiri, Johannes E. Schindelin, Albert Cardona, H. Sebastian Seung. Bioinform., 2017

2,090 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology, Structural Biology

View Paper: https://www.semanticscholar.org/paper/8b27e2fafbe24cf9ce24f308a7e746489ff0dfb8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

1. Curtis T. Rueden
2. Kevin W. Eliceiri
3. Johannes E. Schindelin
4. Mark C. Hiner
5. Alison E. Walter
6. Barry E. DeZonia
7. Ellen T. Arena
8. Stephan Saalfeld

In Molecular Biology

1. Curtis T. Rueden
2. Johannes E. Schindelin
3. Kevin W. Eliceiri
4. Mark C. Hiner
5. Alison E. Walter
6. Barry E. DeZonia
7. Ellen T. Arena
8. Hongyang Cheng

In Structural Biology

1. Kevin W. Eliceiri
2. Curtis T. Rueden
3. Stephan Saalfeld
4. Johannes E. Schindelin
5. Davi D. Bock

Citations in the corpus

• 3D Cell Nuclear Morphology: Microscopy Imaging Dataset And Voxel-Based Morphometry Classification Results (2017, methodology): “Since the Farsight toolkits Nuclear Segmentation is not well suited for segmentation of cellular components other than nuclei, the classification of objects within the nucleus was performed using the Trainable Weka Segmentation [16].”
• ImageJ2: ImageJ for the next generation of scientific image data (2017, methodology): “References for plugins shown: TrackMate [67], MaMuT [150], Multiview Reconstruction [70, 71], MotherMachine Analyzer (MoMA) [151, 152], Sholl Analysis [74], Kymograph Builder [153], Z-Spacing Correction [154], Trainable Weka Segmentation [155], Pendent Drop [156], SciView [157], BigDataViewer [66], ”
• Improving printability of limestone-calcined clay-based cementitious materials by using viscosity-modifying admixture (2020, methodology): “The obtained image stack with the top view of the cylindrical ROI was analyzed via Trainable Weka Segmentation (TWS) from ImageJ (more information: [41,57]).”
• A Complete Electron Microscopy Volume of the Brain of Adult Drosophila melanogaster (2017, methodology): “Although the alignment quality shown here sufficed for manual tracing, further improvements, such as better fine-scale registration near artifacts, may be needed to fully leverage emerging methods for automatic segmentation (Arganda-Carreras et al., 2017; Beier et al., 2017; Januszewski et al., 2016”
• TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines (2022, neutral): “2, Supplementary Videos 1–11, Extended Data Figs.”
• An iterative Bayesian filtering framework for fast and automated calibration of DEM models (2019, methodology): “Empowered by the Trainable Weka Segmentation [66] and MorphoLibJ library [67], the workflow is ad hoc developed to (i) segment multiphase 3DXRCT images (Figs.”