{"year":"2017","publication":"20th International Conference on Medical Image Computing and Computer-Assisted Intervention","status":"public","month":"09","publisher":"Springer Nature","article_processing_charge":"No","alternative_title":["LNCS"],"issue":"Part 1","volume":10433,"type":"conference","date_updated":"2025-01-16T16:04:30Z","date_created":"2024-10-15T11:20:54Z","citation":{"ista":"Alexandroni G, Podolsky Y, Greenspan H, Remez T, Litany O, Bronstein AM, Giryes R. 2017. White matter fiber representation using continuous dictionary learning. 20th International Conference on Medical Image Computing and Computer-Assisted Intervention. MICCAI: Medical Image Computing and Computer-Assisted Intervention, LNCS, vol. 10433, 566–574.","chicago":"Alexandroni, Guy, Yana Podolsky, Hayit Greenspan, Tal Remez, Or Litany, Alex M. Bronstein, and Raja Giryes. “White Matter Fiber Representation Using Continuous Dictionary Learning.” In <i>20th International Conference on Medical Image Computing and Computer-Assisted Intervention</i>, 10433:566–74. Springer Nature, 2017. <a href=\"https://doi.org/10.1007/978-3-319-66182-7_65\">https://doi.org/10.1007/978-3-319-66182-7_65</a>.","mla":"Alexandroni, Guy, et al. “White Matter Fiber Representation Using Continuous Dictionary Learning.” <i>20th International Conference on Medical Image Computing and Computer-Assisted Intervention</i>, vol. 10433, no. Part 1, Springer Nature, 2017, pp. 566–74, doi:<a href=\"https://doi.org/10.1007/978-3-319-66182-7_65\">10.1007/978-3-319-66182-7_65</a>.","short":"G. Alexandroni, Y. Podolsky, H. Greenspan, T. Remez, O. Litany, A.M. Bronstein, R. Giryes, in:, 20th International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer Nature, 2017, pp. 566–574.","ama":"Alexandroni G, Podolsky Y, Greenspan H, et al. White matter fiber representation using continuous dictionary learning. In: <i>20th International Conference on Medical Image Computing and Computer-Assisted Intervention</i>. Vol 10433. Springer Nature; 2017:566-574. doi:<a href=\"https://doi.org/10.1007/978-3-319-66182-7_65\">10.1007/978-3-319-66182-7_65</a>","ieee":"G. Alexandroni <i>et al.</i>, “White matter fiber representation using continuous dictionary learning,” in <i>20th International Conference on Medical Image Computing and Computer-Assisted Intervention</i>, Quebec City, QC, Canada, 2017, vol. 10433, no. Part 1, pp. 566–574.","apa":"Alexandroni, G., Podolsky, Y., Greenspan, H., Remez, T., Litany, O., Bronstein, A. M., &#38; Giryes, R. (2017). White matter fiber representation using continuous dictionary learning. In <i>20th International Conference on Medical Image Computing and Computer-Assisted Intervention</i> (Vol. 10433, pp. 566–574). Quebec City, QC, Canada: Springer Nature. <a href=\"https://doi.org/10.1007/978-3-319-66182-7_65\">https://doi.org/10.1007/978-3-319-66182-7_65</a>"},"language":[{"iso":"eng"}],"_id":"18330","title":"White matter fiber representation using continuous dictionary learning","oa_version":"None","conference":{"location":"Quebec City, QC, Canada","start_date":"2017-09-11","end_date":"2017-09-13","name":"MICCAI: Medical Image Computing and Computer-Assisted Intervention"},"date_published":"2017-09-04T00:00:00Z","page":"566 - 574","author":[{"full_name":"Alexandroni, Guy","first_name":"Guy","last_name":"Alexandroni"},{"last_name":"Podolsky","full_name":"Podolsky, Yana","first_name":"Yana"},{"first_name":"Hayit","full_name":"Greenspan, Hayit","last_name":"Greenspan"},{"last_name":"Remez","first_name":"Tal","full_name":"Remez, Tal"},{"last_name":"Litany","first_name":"Or","full_name":"Litany, Or"},{"last_name":"Bronstein","id":"58f3726e-7cba-11ef-ad8b-e6e8cb3904e6","orcid":"0000-0001-9699-8730","first_name":"Alexander","full_name":"Bronstein, Alexander"},{"full_name":"Giryes, Raja","first_name":"Raja","last_name":"Giryes"}],"quality_controlled":"1","publication_status":"published","doi":"10.1007/978-3-319-66182-7_65","scopus_import":"1","day":"04","extern":"1","abstract":[{"text":"With increasingly sophisticated Diffusion Weighted MRI acquisition methods and modeling techniques, very large sets of streamlines (fibers) are presently generated per imaged brain. These reconstructions of white matter architecture, which are important for human brain research and pre-surgical planning, require a large amount of storage and are often unwieldy and difficult to manipulate and analyze. This work proposes a novel continuous parsimonious framework in which signals are sparsely represented in a dictionary with continuous atoms. The significant innovation in our new methodology is the ability to train such continuous dictionaries, unlike previous approaches that either used pre-fixed continuous transforms or training with finite atoms. This leads to an innovative fiber representation method, which uses Continuous Dictionary Learning to sparsely code each fiber with high accuracy. This method is tested on numerous tractograms produced from the Human Connectome Project data and achieves state-of-the-art performances in compression ratio and reconstruction error.","lang":"eng"}],"user_id":"3E5EF7F0-F248-11E8-B48F-1D18A9856A87","intvolume":"     10433","publication_identifier":{"isbn":["9783319661810"],"issn":["0302-9743"],"eissn":["1611-3349","9783319661827"]}}