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Trainable WEKA Segmentation of Retinal Fundus Images for Global Eye Disease Diagnosis Application

2020 International Journal of Emerging Trends in Engineering Research  
supported by Trainable WEKA segmentation of FIJI Tool.  ...  Image segmentation is a challenging task and quite important in biomedical images for disease identification and measuring the progress of the disease.  ...  Ignacio Arganda-Carreras et al (2014) [4] presented Trainable Weka Segmentation (TWS) is a versatile tool for Microscopy Image segmentation based on pixel classification.  ... 
doi:10.30534/ijeter/2020/136892020 fatcat:wqmkd2oitrbb3fvsgjn32gk4xi

ImageSURF: An ImageJ Plugin for Batch Pixel-Based Image Segmentation Using Random Forests

Aidan O'Mara, Anna E. King, James C. Vickers, Matthew T. K. Kirkcaldie
2017 Journal of Open Research Software  
ImageSURF is a macro-compatible ImageJ2/FIJI plugin for pixel-based image segmentation that considers a range of image derivatives to train pixel classifiers which are then applied to image sets of any  ...  Image segmentation is a necessary step in automated quantitative imaging.  ...  Acknowledgements The authors thank Robert Ollington and Carolyn King for their contributions to the initial stages of this project.  ... 
doi:10.5334/jors.172 fatcat:5v252lfdvfblrhijgzhgvgksx4

Labkit: Labeling and Segmentation Toolkit for Big Image Data [article]

Matthias Arzt, Joran Deschamps, Christopher Schmied, Tobias Pietzsch, Deborah Schmidt, Robert Haase, Florian Jug
2021 bioRxiv   pre-print
We present Labkit, a user-friendly Fiji plugin for the segmentation of microscopy image data.  ...  Furthermore, memory efficient and fast random forest based pixel classification inspired by the Waikato Environment for Knowledge Analysis (Weka) is implemented.  ...  We also want to thank the Scientific Computing Facility at MPI-CBG for giving us access to HPC infrastructure.  ... 
doi:10.1101/2021.10.14.464362 fatcat:ev5ugp3565bhle5rw4rrrjmqwy

The Active Segmentation Platform for Microscopic Image Classification and Segmentation

Sumit K. Vohra, Dimiter Prodanov
2021 Brain Sciences  
However, machine learning only can not address the question as to which features are appropriate for a certain classification problem.  ...  On the other hand, machine learning offers an alternative paradigm where predefined features are combined into different classifiers, providing pixel-level classification and segmentation.  ...  Acknowledgments: The development of the Active Segmentation platform was supported by FWO, the COST action INDEPTH (CA 16212), as well as Google Summer of Code 2016-2021, where the projects were sponsored  ... 
doi:10.3390/brainsci11121645 pmid:34942947 pmcid:PMC8699732 fatcat:ohdjbtweubh3rdiapb2zl26ixe

Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison

Tomas Vicar, Jan Balvan, Josef Jaros, Florian Jug, Radim Kolar, Michal Masarik, Jaromir Gumulec
2019 BMC Bioinformatics  
Background Microscopy has been an important technique for studying biology for decades.  ...  contrast and quantitative phase imaging, and we performed a comprehensive comparison of available segmentation methods applicable for label-free data.  ...  Radim Chmelik from Brno University of Technology for enabling the DIC microscopy in their facility and Tomas Slaby from Tescan a.s., Brno, for their kind help with operating the quantitative phase microscopy  ... 
doi:10.1186/s12859-019-2880-8 fatcat:5obqhpefjnao7lzeocyzzy5ltm

LABKIT: Labeling and Segmentation Toolkit for Big Image Data

Matthias Arzt, Joran Deschamps, Christopher Schmied, Tobias Pietzsch, Deborah Schmidt, Pavel Tomancak, Robert Haase, Florian Jug
2022 Frontiers in Computer Science  
We present LABKIT, a user-friendly Fiji plugin for the segmentation of microscopy image data.  ...  This efficiency is achieved by using ImgLib2 and BigDataViewer as well as a memory efficient and fast implementation of the random forest based pixel classification algorithm as the foundation of our software  ...  We also want to thank the Scientific Computing Facility at MPI-CBG for giving us access to HPC infrastructure.  ... 
doi:10.3389/fcomp.2022.777728 fatcat:uucv3pleivgexmkvhffexvwv5m

A machine learning based approach to the segmentation of micro CT data in archaeological and evolutionary sciences [article]

Thomas O'Mahoney, Lidija Mcknight, Tristan Lowe, Maria Mednikova, Jacob Dunn
2019 bioRxiv   pre-print
We demonstrate that Trainable Weka Segmentation can provide a fast and robust method for segmentation and is as effective as other leading-edge machine learning segmentation techniques.  ...  In this paper, we apply the Trainable Weka Segmentation (a freely available plugin for the multiplatform program ImageJ) to typical datasets found in archaeological and evolutionary sciences.  ...  Trainable Weka Segmentation: a machine learning tool for A., Turner, M., Beeching, L., Bellwood, P., Piper, P., Grono, E., Jones, R., Oxenham, 573 M., Kien, N.K.T., Senden, T., Denham, T., 2017.  ... 
doi:10.1101/859983 fatcat:liyunmbrazc5lke6cjje4ks3de

Automated Cell Foreground–Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data

Guochang Ye, Mehmet Kaya
2022 Bioengineering  
Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy.  ...  Compared to the Trainable Weka Segmentation method, the Empirical Gradient Threshold method, and the ilastik segmentation software, the proposed method achieved better segmentation accuracy (dice coefficient  ...  Acknowledgments: Thanks to Craig Woodworth and Han Deng from Clarkson University for kindly providing these valuable image data in this work.  ... 
doi:10.3390/bioengineering9020081 pmid:35200434 pmcid:PMC8869246 fatcat:uiztioywnzdpnnabv2zt4vt55a

Workflow for Segmentation of Caenorhabditis elegans from Fluorescence Images for the Quantitation of Lipids

Theresa Lehner, Dietmar Pum, Judith M. Rollinger, Benjamin Kirchweger
2021 Applied Sciences  
The segmentation is based on a J48 classifier using pixel entropies and is refined by size-thresholding. The accuracy of segmentation was >90% in our external validation.  ...  In this paper, we present an image-processing workflow that includes machine-learning-based segmentation of C. elegans directly from fluorescence images and quantifies their Nile red lipid-derived fluorescence  ...  Acknowledgments: The authors want to thank Martina Redl and Ruzica Colic for the excellent technical assistance and proofreading of this work.  ... 
doi:10.3390/app112311420 fatcat:ed4o6drsyfdx3lyjza2rwichqa

Semi-Automated 3D Segmentation of Human Skeletal Muscle Using Focused Ion Beam-Scanning Electron Microscopic Images

Brian Caffrey, Alexander V. Maltsev, Marta Gonzalez-Freire, Lisa M. Hartnell, Luigi Ferrucci, Sriram Subramaniam
2019 Journal of Structural Biology  
machine learning Weka segmentation software, which dramatically increases the speed of image analysis.  ...  that possible with light microscopy.  ...  We thank Jessica De Andrade for help with validation of the semi-automated segmentation.  ... 
doi:10.1016/j.jsb.2019.03.008 pmid:30914296 pmcid:PMC6681459 fatcat:4yayervbpbc5revya3kgfsklri

Automated methods for 3D Segmentation of Focused Ion Beam-Scanning Electron Microscopic Images [article]

Brian Caffrey, Alexander V. Maltsev, Marta Gonzalez-Freire, Lisa M. Hartnell, Luigi Ferrucci, Sriram Subramaniam
2019 bioRxiv   pre-print
Here, we present a robust method that enables rapid, large-scale acquisition of data from tissue specimens, combined with an approach for automated data segmentation using machine learning, which dramatically  ...  higher than that possible with light microscopy.  ...  The classifier was trained using all training features available in the "Trainable Weka Segmentation" plugin for ImageJ Fiji, a robust machine learning plugin that is professionally maintained by The University  ... 
doi:10.1101/509232 fatcat:zajhz33dazgipl54kfvheaz3xq

Deep Learning for automated phase segmentation in EBSD maps. A case study in Dual Phase steel microstructures [article]

T Martinez Ostormujof
2021 arXiv   pre-print
Compared to other available approaches in the literature for phase discrimination, the models presented here provided higher accuracies in shorter times.  ...  These promising results open a possibility to work on more complex steel microstructures.  ...  ., “Trainable Weka Segmentation: A machine learning tool for microscopy pixel classification,” Bioinformatics, vol. 33, no. 15, pp. 2424–2426, Aug. 2017. [35] D. Phelan, N. Stanford, and R.  ... 
arXiv:2112.03072v1 fatcat:t26qr2vv2fcfzl7ubx2bbdngse

Content-based Propagation of User Markings for Interactive Segmentation of Patterned Images [article]

Vedrana Andersen Dahl, Monica Jane Emerson, Camilla Himmelstrup Trinderup, Anders Bjorholm Dahl
2020 arXiv   pre-print
We formulate image segmentation as a probabilistic pixel classification problem, and we apply segmentation as a step towards characterising image content.  ...  We demonstrate how this can be a very efficient approach to segmentation through pixel classification.  ...  Acknowledgment This work is supported by The Center for Quantification of Imaging Data from MAX IV (QIM) funded by The Capital Region of Denmark.  ... 
arXiv:1809.02226v3 fatcat:wezd36bhwvbunniwigp4rukylu

High-Throughput Image Analysis of Lipid-Droplet-Bound Mitochondria [article]

Nathanael Miller, Dane Wolf, Nour Alsabeeh, Kiana Mahdaviani, Mayuko Segawa, Marc Liesa, Orian Shirihai
2020 bioRxiv   pre-print
Here, we describe a novel method for segmenting Brown Adipose Tissue (BAT) images.  ...  In addition, we lay out a novel machine-learning-based mitochondrial segmentation method that eliminates the bias of manual mitochondrial segmentation and improves object recognition compared to conventional  ...  Figure 3 . 3 Representation of iterative training of the machine learning classifier using the WEKA trainable segmentation plugin for FIJI.  ... 
doi:10.1101/2020.03.10.985929 fatcat:cj3cji5hujc4tjtgwdzpfw7iwe

From Shallow to Deep: Exploiting Feature-Based Classifiers for Domain Adaptation in Semantic Segmentation

Alex Matskevych, Adrian Wolny, Constantin Pape, Anna Kreshuk
2022 Frontiers in Computer Science  
The remarkable performance of Convolutional Neural Networks on image segmentation tasks comes at the cost of a large amount of pixelwise annotated images that have to be segmented for training.  ...  This work bridges the two approaches in a transfer learning setting.  ...  Trainable Weka segmentation: a machine learning tool for microscopy pixel classification.  ... 
doi:10.3389/fcomp.2022.805166 fatcat:upfjbzvgl5gc3go6opyfkd642m
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