Ilastik: Interactive learning and segmentation toolkit

By Christoph Sommer, Christoph Nikolas Straehle, Ullrich Köthe, Fred A. Hamprecht. 2011

1,233 citations recorded by the source corpus.

Subfields: Biophysics, Computer Vision and Pattern Recognition, Media Technology, Software Engineering

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Citations in the corpus

Interactive Object Counting (2014, methodology): “Finally, we propose an online codebook learning [20] that, in real-time, re-estimates low-level feature encoding as the user annotates progressively larger parts of an image.”
Keeping count: Leveraging temporal context to count heavily overlapping objects (2013, methodology): “For detection and segmentation of the foreground regions we use the open-source software ILASTIK [12].”
MorphoGraphX: A platform for quantifying morphogenesis in 4D (2015, methodology): “We have also included a range of standard 3D image processing tools, similar to those available in many other softwares ( Fernandez et al., 2010 ; Peng et al., 2010 ; Sommer et al., 2011 ; Federici et al., 2012 ; Mosaliganti et al., 2012 ; Schmidt et al., 2014 ).”
Asymmetric Cuts: Joint Image Labeling and Partitioning (2014, methodology): “6: From the raw volume image (a), a mitochondria versus background probability map (b) was obtained using ilastik [34].”
Content-aware image restoration: pushing the limits of fluorescence microscopy (2018, methodology): “Since the purpose of the fruit ﬂy data is to segment and track nuclei, we applied a common segmentation pipeline [44] to the raw and restored images, and observed that the fraction of incorrectly identiﬁed nuclei was lowered from 1 .”
ilastik: interactive machine learning for (bio)image analysis (2019, background): “(3), aims to address both the data and the methods/parameters deluge by formulating several popular image analysis problems in the paradigm of interactively supervised machine learning.”
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (2015, methodology): “Printed on acid-free paper Springer International Publishing AG Switzerland is part of Springer Science+Business Media (www.springer.com) Preface In 2015, the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2015) was held in Munich, Germany.”
Machine learning applications in cell image analysis (2017, background): “14,18,49,50 Active learning is a particular type of semi-automated system, whereby the human is iteratively asked to provide labels for instances.”
Data-analysis strategies for image-based cell profiling (2017, background): “Metrics can be computed to detect blurring, including the ratio of the mean and the s.d. of each image’s pixel intensities, the normalized measure of the intensity variance 37 , and the image correlation across subregions of the image 38 .”