Anatomical Sketch Understanding: Recognizing Explicit and Implicit Structure
Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving...
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creator | Haddawy, Peter Dailey, Matthew Kaewruen, Ploen Sarakhette, Natapope |
description | Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician. |
doi_str_mv | 10.1007/11527770_47 |
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Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. 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Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. We evaluate the accuracy of the recognition algorithm on sketches obtained from medical students. We evaluate the part identification algorithm by comparing its results to the judgment of an experienced physician.</description><subject>Anatomical Part</subject><subject>External View</subject><subject>Intelligent Tutoring System</subject><subject>Primitive Component</subject><subject>Shape Context</subject><issn>0302-9743</issn><issn>1611-3349</issn><isbn>9783540278313</isbn><isbn>3540278311</isbn><isbn>3540318844</isbn><isbn>9783540318842</isbn><fulltext>true</fulltext><rsrctype>book_chapter</rsrctype><creationdate>2005</creationdate><recordtype>book_chapter</recordtype><sourceid/><recordid>eNpNkE1Lw0AYhNcvsNac_AO5eoi-b3Y3u-utlKqFgGDtOexXamyalOwWxF9vij04l2F4YBiGkDuEBwQQj4g8F0JAxcQZuaGcAUUpGTsnEywQM0qZuiCJEvLI8tGQXpIJUMgzJRi9JkkIXzCKYkE5nZBy1unY7xqr23S19dF-puvO-SFE3bmm2zyl7972m675GUO6-N63jW1iOsJ0uTuFVRwONh4Gf0uuat0Gn5x8StbPi4_5a1a-vSznszILKFXM0Bn0oI0EmYucGeFRFlwVzKMwFmruRKGVlrXVLodCO-2YktJqxYWRxtApuf_rDfthnOWHyvT9NlQI1fGm6t9N9BeitFXi</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Haddawy, Peter</creator><creator>Dailey, Matthew</creator><creator>Kaewruen, Ploen</creator><creator>Sarakhette, Natapope</creator><general>Springer Berlin Heidelberg</general><scope/></search><sort><creationdate>2005</creationdate><title>Anatomical Sketch Understanding: Recognizing Explicit and Implicit Structure</title><author>Haddawy, Peter ; Dailey, Matthew ; Kaewruen, Ploen ; Sarakhette, Natapope</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-s189t-1db1e0ab8082724b7e1865964e17bc0f5d76a9a8fcad206adad4988ca957b8bb3</frbrgroupid><rsrctype>book_chapters</rsrctype><prefilter>book_chapters</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Anatomical Part</topic><topic>External View</topic><topic>Intelligent Tutoring System</topic><topic>Primitive Component</topic><topic>Shape Context</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Haddawy, Peter</creatorcontrib><creatorcontrib>Dailey, Matthew</creatorcontrib><creatorcontrib>Kaewruen, Ploen</creatorcontrib><creatorcontrib>Sarakhette, Natapope</creatorcontrib></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Haddawy, Peter</au><au>Dailey, Matthew</au><au>Kaewruen, Ploen</au><au>Sarakhette, Natapope</au><au>Keravnou, Elpida T.</au><au>Miksch, Silvia</au><au>Hunter, Jim</au><format>book</format><genre>bookitem</genre><ristype>CHAP</ristype><atitle>Anatomical Sketch Understanding: Recognizing Explicit and Implicit Structure</atitle><btitle>Artificial Intelligence in Medicine</btitle><seriestitle>Lecture Notes in Computer Science</seriestitle><date>2005</date><risdate>2005</risdate><spage>343</spage><epage>352</epage><pages>343-352</pages><issn>0302-9743</issn><eissn>1611-3349</eissn><isbn>9783540278313</isbn><isbn>3540278311</isbn><eisbn>3540318844</eisbn><eisbn>9783540318842</eisbn><abstract>Sketching is ubiquitous in medicine. Physicians commonly use sketches as part of their note taking in patient records and to help convey diagnoses and treatments to patients. Medical students frequently use sketches to help them think through clinical problems in individual and group problem solving. Applications ranging from automated patient records to medical education software could benefit greatly from the richer and more natural interfaces that would be enabled by the ability to understand sketches. In this paper we take the first steps toward developing a system that can understand anatomical sketches. Understanding an anatomical sketch requires the ability to recognize what anatomical structure has been sketched and from what view (e.g. parietal view of the brain), as well as to identify the anatomical parts and their locations in the sketch (e.g. parts of the brain), even if they have not been explicitly drawn. We present novel algorithms for sketch recognition and for part identification. 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language | eng |
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source | Springer Books |
subjects | Anatomical Part External View Intelligent Tutoring System Primitive Component Shape Context |
title | Anatomical Sketch Understanding: Recognizing Explicit and Implicit Structure |
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