A digital brain atlas for surgical planning, model-driven segmentation, and teaching
We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a comb...
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Veröffentlicht in: | IEEE transactions on visualization and computer graphics 1996-09, Vol.2 (3), p.232-241 |
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creator | Kikinis, R. Shenton, M.E. Iosifescu, D.V. McCarley, R.W. Saiviroonporn, P. Hokama, H.H. Robatino, A. Metcalf, D. Wible, C.G. Portas, C.M. Donnino, R.M. Jolesz, F.A. |
description | We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images. |
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It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images.</description><identifier>ISSN: 1077-2626</identifier><identifier>EISSN: 1941-0506</identifier><identifier>DOI: 10.1109/2945.537306</identifier><identifier>CODEN: ITVGEA</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Artificial intelligence ; Biological and medical sciences ; Biomedical imaging ; Brain modeling ; Computer science; control theory; systems ; Computerized, statistical medical data processing and models in biomedicine ; Data visualization ; Education ; Exact sciences and technology ; Humans ; Image segmentation ; Laboratories ; Learning and adaptive systems ; Magnetic resonance imaging ; Medical management aid. Diagnosis aid ; Medical sciences ; Neuroscience ; Pattern recognition. Digital image processing. Computational geometry ; Surgery</subject><ispartof>IEEE transactions on visualization and computer graphics, 1996-09, Vol.2 (3), p.232-241</ispartof><rights>1997 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c409t-8eb74410a44921fc124dd0747a8dd8f3d8dd823815937364aff0577b74c479a3</citedby><cites>FETCH-LOGICAL-c409t-8eb74410a44921fc124dd0747a8dd8f3d8dd823815937364aff0577b74c479a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/537306$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,314,776,780,785,786,792,23909,23910,25118,27901,27902,54733</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/537306$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=2483865$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Kikinis, R.</creatorcontrib><creatorcontrib>Shenton, M.E.</creatorcontrib><creatorcontrib>Iosifescu, D.V.</creatorcontrib><creatorcontrib>McCarley, R.W.</creatorcontrib><creatorcontrib>Saiviroonporn, P.</creatorcontrib><creatorcontrib>Hokama, H.H.</creatorcontrib><creatorcontrib>Robatino, A.</creatorcontrib><creatorcontrib>Metcalf, D.</creatorcontrib><creatorcontrib>Wible, C.G.</creatorcontrib><creatorcontrib>Portas, C.M.</creatorcontrib><creatorcontrib>Donnino, R.M.</creatorcontrib><creatorcontrib>Jolesz, F.A.</creatorcontrib><title>A digital brain atlas for surgical planning, model-driven segmentation, and teaching</title><title>IEEE transactions on visualization and computer graphics</title><addtitle>TVCG</addtitle><description>We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images.</description><subject>Applied sciences</subject><subject>Artificial intelligence</subject><subject>Biological and medical sciences</subject><subject>Biomedical imaging</subject><subject>Brain modeling</subject><subject>Computer science; control theory; systems</subject><subject>Computerized, statistical medical data processing and models in biomedicine</subject><subject>Data visualization</subject><subject>Education</subject><subject>Exact sciences and technology</subject><subject>Humans</subject><subject>Image segmentation</subject><subject>Laboratories</subject><subject>Learning and adaptive systems</subject><subject>Magnetic resonance imaging</subject><subject>Medical management aid. Diagnosis aid</subject><subject>Medical sciences</subject><subject>Neuroscience</subject><subject>Pattern recognition. Digital image processing. Computational geometry</subject><subject>Surgery</subject><issn>1077-2626</issn><issn>1941-0506</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><recordid>eNqFkL1PwzAQxS0EEqUwsTF5QCw0xXac2B6rii-pEkv36OqPYJQ6xU6R-O9xlaor0510v_fu7iF0S8mcUqKemOLVvCpFSeozNKGK04JUpD7PPRGiYDWrL9FVSl-EUM6lmqD1Ahvf-gE6vIngA4ahg4RdH3Hax9brPNh1EIIP7Qxve2O7wkT_YwNOtt3aMMDg-zDDEAweLOjPDF6jCwddsjfHOkXrl-f18q1Yfby-LxerQnOihkLajeCcEuBcMeo0ZdwYIrgAaYx0pTkUVkpaqfxSzcE5UgmRRZoLBeUUPYy2u9h_720amq1P2nb5XNvvU8Mko5Us5b9gpkjNaZnBxxHUsU8pWtfsot9C_G0oaQ4JN4eEmzHhTN8fbSHlnFyEoH06SRjPu-sqY3cj5q21p-nR4w9304G9</recordid><startdate>19960901</startdate><enddate>19960901</enddate><creator>Kikinis, R.</creator><creator>Shenton, M.E.</creator><creator>Iosifescu, D.V.</creator><creator>McCarley, R.W.</creator><creator>Saiviroonporn, P.</creator><creator>Hokama, H.H.</creator><creator>Robatino, A.</creator><creator>Metcalf, D.</creator><creator>Wible, C.G.</creator><creator>Portas, C.M.</creator><creator>Donnino, R.M.</creator><creator>Jolesz, F.A.</creator><general>IEEE</general><general>IEEE Computer Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>7SC</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>19960901</creationdate><title>A digital brain atlas for surgical planning, model-driven segmentation, and teaching</title><author>Kikinis, R. ; Shenton, M.E. ; Iosifescu, D.V. ; McCarley, R.W. ; Saiviroonporn, P. ; Hokama, H.H. ; Robatino, A. ; Metcalf, D. ; Wible, C.G. ; Portas, C.M. ; Donnino, R.M. ; Jolesz, F.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c409t-8eb74410a44921fc124dd0747a8dd8f3d8dd823815937364aff0577b74c479a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Applied sciences</topic><topic>Artificial intelligence</topic><topic>Biological and medical sciences</topic><topic>Biomedical imaging</topic><topic>Brain modeling</topic><topic>Computer science; control theory; systems</topic><topic>Computerized, statistical medical data processing and models in biomedicine</topic><topic>Data visualization</topic><topic>Education</topic><topic>Exact sciences and technology</topic><topic>Humans</topic><topic>Image segmentation</topic><topic>Laboratories</topic><topic>Learning and adaptive systems</topic><topic>Magnetic resonance imaging</topic><topic>Medical management aid. Diagnosis aid</topic><topic>Medical sciences</topic><topic>Neuroscience</topic><topic>Pattern recognition. Digital image processing. Computational geometry</topic><topic>Surgery</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Kikinis, R.</creatorcontrib><creatorcontrib>Shenton, M.E.</creatorcontrib><creatorcontrib>Iosifescu, D.V.</creatorcontrib><creatorcontrib>McCarley, R.W.</creatorcontrib><creatorcontrib>Saiviroonporn, P.</creatorcontrib><creatorcontrib>Hokama, H.H.</creatorcontrib><creatorcontrib>Robatino, A.</creatorcontrib><creatorcontrib>Metcalf, D.</creatorcontrib><creatorcontrib>Wible, C.G.</creatorcontrib><creatorcontrib>Portas, C.M.</creatorcontrib><creatorcontrib>Donnino, R.M.</creatorcontrib><creatorcontrib>Jolesz, F.A.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on visualization and computer graphics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Kikinis, R.</au><au>Shenton, M.E.</au><au>Iosifescu, D.V.</au><au>McCarley, R.W.</au><au>Saiviroonporn, P.</au><au>Hokama, H.H.</au><au>Robatino, A.</au><au>Metcalf, D.</au><au>Wible, C.G.</au><au>Portas, C.M.</au><au>Donnino, R.M.</au><au>Jolesz, F.A.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A digital brain atlas for surgical planning, model-driven segmentation, and teaching</atitle><jtitle>IEEE transactions on visualization and computer graphics</jtitle><stitle>TVCG</stitle><date>1996-09-01</date><risdate>1996</risdate><volume>2</volume><issue>3</issue><spage>232</spage><epage>241</epage><pages>232-241</pages><issn>1077-2626</issn><eissn>1941-0506</eissn><coden>ITVGEA</coden><abstract>We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this Information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization in surgical planning far model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/2945.537306</doi><tpages>10</tpages></addata></record> |
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subjects | Applied sciences Artificial intelligence Biological and medical sciences Biomedical imaging Brain modeling Computer science control theory systems Computerized, statistical medical data processing and models in biomedicine Data visualization Education Exact sciences and technology Humans Image segmentation Laboratories Learning and adaptive systems Magnetic resonance imaging Medical management aid. Diagnosis aid Medical sciences Neuroscience Pattern recognition. Digital image processing. Computational geometry Surgery |
title | A digital brain atlas for surgical planning, model-driven segmentation, and teaching |
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