2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro 2011
DOI: 10.1109/isbi.2011.5872394
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Ilastik: Interactive learning and segmentation toolkit

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Cited by 1,043 publications
(1,004 citation statements)
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References 15 publications
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“…To separate the epoxy resin matrix from the actual sample material, we employed a supervised pixel classification based on 12 C − (indicative for resin) as well as 16 O − and 12 C 14 N − (indicative for mineral and organic surfaces) secondary ion distributions. The images were segmented using a machine-learning algorithm implemented in Ilastik 1.2 [26]. The resulting segment masks were used to exclude the resin areas from further evaluations (see the scheme of image analysis, Figure 2).…”
Section: Image and Statistical Analysesmentioning
confidence: 99%
“…To separate the epoxy resin matrix from the actual sample material, we employed a supervised pixel classification based on 12 C − (indicative for resin) as well as 16 O − and 12 C 14 N − (indicative for mineral and organic surfaces) secondary ion distributions. The images were segmented using a machine-learning algorithm implemented in Ilastik 1.2 [26]. The resulting segment masks were used to exclude the resin areas from further evaluations (see the scheme of image analysis, Figure 2).…”
Section: Image and Statistical Analysesmentioning
confidence: 99%
“…Parameters or seeds for segmentation are often manually selected on a per-image basis based on a preview of the result, or may be selected, reviewed and refined in an unstructured iterative process [3].…”
Section: Introductionmentioning
confidence: 99%
“…Open-source trainable segmentation tools, such as Ilastik [3] or the Trainable Segmentation [6] plugin for ImageJ [7], address many of these issues by using supervised machine learning algorithms to study 'training set' of pixels, which have been manually assigned class annotations, and create a model (' classifier') to reliably discriminate between these classes. The context of each pixel (e.g., intensity, texture, edges, entropy) can be considered, making the classifier more robust to image artifacts and intensity shifts [3].…”
Section: Introductionmentioning
confidence: 99%
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“…Compared to other commonly-cited open-source biological image classification software like Ilastik (Sommer et al, 2011), CellCognition (Held et al, 2010) and WND-CHARM (Orlov et al, 2008), CellProfiler Analyst has the advantage of containing companion visualization tools, being suitable for high-throughput datasets, having multiple classifier options, and allowing both cell and fieldof-view classification. Advanced Cell Classifier (Horvath et al, 2011) shares many of the classification features of CellProfiler Analyst, but it lacks HCS data exploration and visualization tools.…”
Section: Introductionmentioning
confidence: 99%