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Development of a semi-automated segmentation framework for thoracic-abdominal organs
Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches i...
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creator | Rahni, Ashrani Aizzuddin Abd Lewis, Emma Wells, Kevin |
description | Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches require an atlas which may not be generic enough. This paper proposes a semi-automated data-driven segmentation framework of thoracic CT scans. The preliminary results of the framework is presented and discussed with proposals for future work. |
doi_str_mv | 10.1109/ICSIPA.2013.6708009 |
format | conference_proceeding |
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A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches require an atlas which may not be generic enough. This paper proposes a semi-automated data-driven segmentation framework of thoracic CT scans. 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The preliminary results of the framework is presented and discussed with proposals for future work.</description><subject>Biomedical imaging</subject><subject>Computed tomography</subject><subject>Image segmentation</subject><subject>Liver</subject><subject>Lungs</subject><subject>Three-dimensional displays</subject><isbn>1479902691</isbn><isbn>9781479902699</isbn><isbn>1479902675</isbn><isbn>9781479902675</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotj81Kw0AURseFoNY-QTfzAon3ZvI3yxL_AgUF67rcydypo0mmTKLi22uxq4_DgQOfECuEFBH0Tdu8tM_rNANUaVlBDaDPxBXmldaQlRovxHKa3gEAqwpryC7F9pa_uA-HgcdZBidJTjz4hD7nMNDM9g_3R0ezD6N0kQb-DvFDuhDl_BYidb5LyNgw-JF6GeKexulanDvqJ16ediFe7--2zWOyeXpom_Um8VgVc-JQK2BX1aYm1VmDhjWR0YXpcgBTkoJCs9PWca7YsqtLRKNdWbjCIli1EKv_rmfm3SH6geLP7nRc_QJEv1Gt</recordid><startdate>201310</startdate><enddate>201310</enddate><creator>Rahni, Ashrani Aizzuddin Abd</creator><creator>Lewis, Emma</creator><creator>Wells, Kevin</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201310</creationdate><title>Development of a semi-automated segmentation framework for thoracic-abdominal organs</title><author>Rahni, Ashrani Aizzuddin Abd ; Lewis, Emma ; Wells, Kevin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-f1930ef78b8a3cdb1be9aab95bc400b6a3059ef9dfe43edef8611b9f65f5d10d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Biomedical imaging</topic><topic>Computed tomography</topic><topic>Image segmentation</topic><topic>Liver</topic><topic>Lungs</topic><topic>Three-dimensional displays</topic><toplevel>online_resources</toplevel><creatorcontrib>Rahni, Ashrani Aizzuddin Abd</creatorcontrib><creatorcontrib>Lewis, Emma</creatorcontrib><creatorcontrib>Wells, Kevin</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Rahni, Ashrani Aizzuddin Abd</au><au>Lewis, Emma</au><au>Wells, Kevin</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Development of a semi-automated segmentation framework for thoracic-abdominal organs</atitle><btitle>2013 IEEE International Conference on Signal and Image Processing Applications</btitle><stitle>ICSIPA</stitle><date>2013-10</date><risdate>2013</risdate><spage>232</spage><epage>236</epage><pages>232-236</pages><eisbn>1479902691</eisbn><eisbn>9781479902699</eisbn><eisbn>1479902675</eisbn><eisbn>9781479902675</eisbn><abstract>Due to the increasing amount of data available from medical imaging procedures and also the increase in computing power, there has been a rise in the automation of the analysis of such data. A crucial step in the automation of such procedures is accurate segmentation of anatomy. Popular approaches include model based segmentation. However, these approaches require an atlas which may not be generic enough. This paper proposes a semi-automated data-driven segmentation framework of thoracic CT scans. The preliminary results of the framework is presented and discussed with proposals for future work.</abstract><pub>IEEE</pub><doi>10.1109/ICSIPA.2013.6708009</doi><tpages>5</tpages></addata></record> |
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subjects | Biomedical imaging Computed tomography Image segmentation Liver Lungs Three-dimensional displays |
title | Development of a semi-automated segmentation framework for thoracic-abdominal organs |
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