{"version":1,"kind":"Article","sha256":"","slug":"579","location":"","dependencies":[],"doi":"10.54294/zf8wp1","frontmatter":{"title":"An iterative Bayesian approach for liver analysis: tumors validation study","abstract":"We present a new method for the simultaneous, nearly automatic segmentation of liver contours, vessels,\r\nand tumors from abdominal CTA scans. The method repeatedly applies multi-resolution, multi-class\r\nsmoothed Bayesian classification followed by morphological adjustment and active contours refinement.\r\nIt uses multi-class and voxel neighborhood information to compute an accurate intensity distribution\r\nfunction for each class. Only one user-defined voxel seed for the liver and additional seeds according\r\nto the number of tumors inside the liver are required for initialization. The algorithm do not require\r\nmanual adjustment of internal parameters. In this work, a retrospective study on a validated clinical\r\ndataset totaling 20 tumors from 9 patients CTAs� was performed. An aggregated competition score of\r\n61 was obtained on the test set of this database. In addition we measured the robustness of our algorithm\r\nto different seeds initializations. These results suggest that our method is clinically applicable, accurate,\r\nefficient, and robust to seed selection compared to manually generated ground truth segmentation and to\r\nother semi-automatic segmentation methods.","license":"You are licensing your work to Kitware Inc. under the\nCreative Commons Attribution License Version 3.0.\n\nKitware Inc. agrees to the following:\n\nKitware is free\n * to copy, distribute, display, and perform the work\n * to make derivative works\n * to make commercial use of the work\n\nUnder the following conditions:\n\\\"by Attribution\\\" - Kitware must attribute the work in the manner specified by the author or licensor.\n\n * For any reuse or distribution, they must make clear to others the license terms of this work.\n * Any of these conditions can be waived if they get permission from the copyright holder.\n\nYour fair use and other rights are in no way affected by the above.\n\nThis is a human-readable summary of the Legal Code (the full license) available at\nhttp://creativecommons.org/licenses/by/3.0/legalcode","keywords":["tumor segmentation","Liver segmentation"],"authors":[{"name":"Taieb, Yoav","affiliations":[]},{"name":"Eliassaf, Ofer","affiliations":[]},{"name":"Freiman, Moti","email":"freiman@cs.huji.ac.il","affiliations":["Hebrew University"],"corresponding":true},{"name":"Joskowicz, Leo","email":"josko@cs.huji.ac.il","affiliations":[]},{"name":"Sosna, Jacob","affiliations":[]}],"date_submitted":"2008-07-06T04:29:59Z","external_publication_id":579,"revision_cids":["bafkreibcwbvocdv5ljyf6fd3y5nvdmotnk7ivjx2p5me4sb4bc32wi34ii"]},"mdast":{"type":"root"},"downloads":[{"url":"https://ipfs.desci.com/ipfs/bafkreiblvdc3km2yukt4i7w7jkiy7ngn2kocvqut7midol4h6is46fvcfi","title":"root/reviews.md","filename":"reviews.md","extra":{"size_bytes":1321,"type":"file"}},{"url":"https://ipfs.desci.com/ipfs/bafkreiennufjnlyf6ztwowjiimatcs33jyn3jq73g2kapa26hgdzzhp67q","title":"root/insight-journal-metadata.json","filename":"insight-journal-metadata.json","extra":{"size_bytes":11162,"type":"file"}},{"url":"https://dweb.link/ipfs/bafybeibps336a5qttn2e5c37zhptjhyjnvae4b5cgztr2u7btrrlcc4qla","title":"root/article.pdf","filename":"article.pdf","extra":{"size_bytes":1395710,"type":"file"}}],"references":{"cite":{"order":["ref1","ref2","ref3","ref4","ref5","ref6","ref7","ref8","ref9","ref10","ref11","ref12","ref13","ref14","ref15","ref16","ref17","ref18","ref19","ref20"]},"data":{"ref1":{"label":"ref1","enumerator":"1","url":"https://doi.org/10.1007/978-3-540-75757-3_11","html":"Automated segmentation of the liver from 3D CT images using probabilistic atlas and multi-level statistical shape model+of LNCS+4791+86+93+2007+T. 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