[{"keyword":["image segmentation","biomedical imaging","three dimensional displays","neurons","retina","observers","image color analysis"],"extern":"1","title":"Ilastik: Interactive learning and segmentation toolkit","publisher":"Institute of Electrical and Electronics Engineers","publication_identifier":{"eissn":["1945-8452"],"issn":["1945-7928"],"isbn":["978-1-4244-4127-3"]},"date_published":"2011-06-09T00:00:00Z","publication":"2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro","author":[{"first_name":"Christoph M","last_name":"Sommer","id":"4DF26D8C-F248-11E8-B48F-1D18A9856A87","full_name":"Sommer, Christoph M","orcid":"0000-0003-1216-9105"},{"first_name":"Christoph","full_name":"Straehle, Christoph","last_name":"Straehle"},{"full_name":"Köthe, Ullrich","first_name":"Ullrich","last_name":"Köthe"},{"first_name":"Fred A.","last_name":"Hamprecht","full_name":"Hamprecht, Fred A."}],"main_file_link":[{"url":"https://www.researchgate.net/publication/224241106_Ilastik_Interactive_learning_and_segmentation_toolkit","open_access":"1"}],"oa_version":"Preprint","conference":{"start_date":"2011-03-30","end_date":"2011-04-02","name":"ISBI: International Symposium on Biomedical Imaging","location":"Chicago, Illinois, USA"},"oa":1,"_id":"9943","language":[{"iso":"eng"}],"user_id":"8b945eb4-e2f2-11eb-945a-df72226e66a9","month":"06","department":[{"_id":"Bio"}],"status":"public","day":"09","date_updated":"2023-02-23T14:13:38Z","publication_status":"published","abstract":[{"text":"Segmentation is the process of partitioning digital images into meaningful regions. The analysis of biological high content images often requires segmentation as a first step. We propose ilastik as an easy-to-use tool which allows the user without expertise in image processing to perform segmentation and classification in a unified way. ilastik learns from labels provided by the user through a convenient mouse interface. Based on these labels, ilastik infers a problem specific segmentation. A random forest classifier is used in the learning step, in which each pixel's neighborhood is characterized by a set of generic (nonlinear) features. ilastik supports up to three spatial plus one spectral dimension and makes use of all dimensions in the feature calculation. ilastik provides realtime feedback that enables the user to interactively refine the segmentation result and hence further fine-tune the classifier. An uncertainty measure guides the user to ambiguous regions in the images. Real time performance is achieved by multi-threading which fully exploits the capabilities of modern multi-core machines. Once a classifier has been trained on a set of representative images, it can be exported and used to automatically process a very large number of images (e.g. using the CellProfiler pipeline). ilastik is an open source project and released under the BSD license at www.ilastik.org.","lang":"eng"}],"fulldoi":"https://doi.org/10.1109/isbi.2011.5872394","citation":{"ista":"Sommer CM, Straehle C, Köthe U, Hamprecht FA. 2011. Ilastik: Interactive learning and segmentation toolkit. 2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro. ISBI: International Symposium on Biomedical Imaging.","apa":"Sommer, C. M., Straehle, C., Köthe, U., &#38; Hamprecht, F. A. (2011). Ilastik: Interactive learning and segmentation toolkit. In <i>2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro</i>. Chicago, Illinois, USA: Institute of Electrical and Electronics Engineers. <a href=\"https://doi.org/10.1109/isbi.2011.5872394\">https://doi.org/10.1109/isbi.2011.5872394</a>","ieee":"C. M. Sommer, C. Straehle, U. Köthe, and F. A. Hamprecht, “Ilastik: Interactive learning and segmentation toolkit,” in <i>2011 IEEE International Symposium on Biomedical Imaging: from Nano to Micro</i>, Chicago, Illinois, USA, 2011.","chicago":"Sommer, Christoph M, Christoph Straehle, Ullrich Köthe, and Fred A. Hamprecht. “Ilastik: Interactive Learning and Segmentation Toolkit.” In <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro</i>. Institute of Electrical and Electronics Engineers, 2011. <a href=\"https://doi.org/10.1109/isbi.2011.5872394\">https://doi.org/10.1109/isbi.2011.5872394</a>.","mla":"Sommer, Christoph M., et al. “Ilastik: Interactive Learning and Segmentation Toolkit.” <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro</i>, Institute of Electrical and Electronics Engineers, 2011, doi:<a href=\"https://doi.org/10.1109/isbi.2011.5872394\">10.1109/isbi.2011.5872394</a>.","ama":"Sommer CM, Straehle C, Köthe U, Hamprecht FA. Ilastik: Interactive learning and segmentation toolkit. In: <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro</i>. Institute of Electrical and Electronics Engineers; 2011. doi:<a href=\"https://doi.org/10.1109/isbi.2011.5872394\">10.1109/isbi.2011.5872394</a>","short":"C.M. Sommer, C. Straehle, U. Köthe, F.A. Hamprecht, in:, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Micro, Institute of Electrical and Electronics Engineers, 2011."},"date_created":"2021-08-19T11:49:58Z","year":"2011","doi":"10.1109/isbi.2011.5872394","article_processing_charge":"No","type":"conference","quality_controlled":"1"},{"conference":{"location":" Chicago, IL, United States","name":"8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro","end_date":"2011-04-02","start_date":"2011-03-30"},"oa_version":"None","_id":"18406","article_number":"5872619","scopus_import":"1","publication":"2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro","author":[{"last_name":"Michel","first_name":"Fabrice","full_name":"Michel, Fabrice"},{"first_name":"Michael","last_name":"Bronstein","full_name":"Bronstein, Michael"},{"first_name":"Alexander","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","full_name":"Bronstein, Alexander","last_name":"Bronstein","orcid":"0000-0001-9699-8730"},{"first_name":"Nikos","full_name":"Paragios, Nikos","last_name":"Paragios"}],"date_published":"2011-06-09T00:00:00Z","extern":"1","publication_identifier":{"eissn":["1945-8452"],"isbn":["9781424441280"]},"publisher":"IEEE","title":"Boosted metric learning for 3D multi-modal deformable registration","quality_controlled":"1","article_processing_charge":"No","type":"conference","date_created":"2024-10-15T11:20:54Z","doi":"10.1109/isbi.2011.5872619","year":"2011","day":"09","date_updated":"2024-12-04T11:55:42Z","citation":{"short":"F. Michel, M. Bronstein, A.M. Bronstein, N. Paragios, in:, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, IEEE, 2011.","ama":"Michel F, Bronstein M, Bronstein AM, Paragios N. Boosted metric learning for 3D multi-modal deformable registration. In: <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</i>. IEEE; 2011. doi:<a href=\"https://doi.org/10.1109/isbi.2011.5872619\">10.1109/isbi.2011.5872619</a>","chicago":"Michel, Fabrice, Michael Bronstein, Alex M. Bronstein, and Nikos Paragios. “Boosted Metric Learning for 3D Multi-Modal Deformable Registration.” In <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</i>. IEEE, 2011. <a href=\"https://doi.org/10.1109/isbi.2011.5872619\">https://doi.org/10.1109/isbi.2011.5872619</a>.","mla":"Michel, Fabrice, et al. “Boosted Metric Learning for 3D Multi-Modal Deformable Registration.” <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</i>, 5872619, IEEE, 2011, doi:<a href=\"https://doi.org/10.1109/isbi.2011.5872619\">10.1109/isbi.2011.5872619</a>.","ista":"Michel F, Bronstein M, Bronstein AM, Paragios N. 2011. Boosted metric learning for 3D multi-modal deformable registration. 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro. 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 5872619.","apa":"Michel, F., Bronstein, M., Bronstein, A. M., &#38; Paragios, N. (2011). Boosted metric learning for 3D multi-modal deformable registration. In <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</i>.  Chicago, IL, United States: IEEE. <a href=\"https://doi.org/10.1109/isbi.2011.5872619\">https://doi.org/10.1109/isbi.2011.5872619</a>","ieee":"F. Michel, M. Bronstein, A. M. Bronstein, and N. Paragios, “Boosted metric learning for 3D multi-modal deformable registration,” in <i>2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro</i>,  Chicago, IL, United States, 2011."},"fulldoi":"https://doi.org/10.1109/isbi.2011.5872619","abstract":[{"text":"Defining a suitable metric is one of the biggest challenges in deformable image fusion from different modalities. In this paper, we propose a novel approach for multi-modal metric learning in the deformable registration framework that consists of embedding data from both modalities into a common metric space whose metric is used to parametrize the similarity. Specifically, we use image representation in the Fourier/Gabor space which introduces invariance to the local pose parameters, and the Hamming metric as the target embedding space, which allows constructing the embedding using boosted learning algorithms. The resulting metric is incorporated into a discrete optimization framework. Very promising results demonstrate the potential of the proposed method.","lang":"eng"}],"publication_status":"published","user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","month":"06","language":[{"iso":"eng"}],"status":"public"}]