Analyzing Tubular Tissue in Histopathological Thin Sections
We propose a method for automatic segmentation of tubules in the stained thin sections of various tissue types. Tubules consist of one or more layers of cells surrounding a cavity. The segmented tubules can be used to study the morphology of the tissue. Some research has been done to automatically e...
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creator | Fakhrzadeh, A. Spörndly-Nees, E. Holm, L. Hendriks, C. L. L. |
description | We propose a method for automatic segmentation of tubules in the stained thin sections of various tissue types. Tubules consist of one or more layers of cells surrounding a cavity. The segmented tubules can be used to study the morphology of the tissue. Some research has been done to automatically estimate the density of tubules. To the best of our knowledge, no one has been able to, fully automatically, segment the whole tubule. Usually the border between tubules is subtle and appears broken in a straight-forward segmentation. Here we suggest delineating these borders using the geodesic distance transform. We apply this method on images of Periodic Acid Shiffs (PAS) stained thin sections of testicular tissue, delineating 89% of the tubules correctly. |
doi_str_mv | 10.1109/DICTA.2012.6411735 |
format | Conference Proceeding |
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We apply this method on images of Periodic Acid Shiffs (PAS) stained thin sections of testicular tissue, delineating 89% of the tubules correctly.</description><subject>Glands</subject><subject>Image color analysis</subject><subject>Image edge detection</subject><subject>Image segmentation</subject><subject>Level set</subject><subject>Medical Image Processing</subject><subject>Medicinsk bildbehandling</subject><subject>Morphology</subject><subject>Transforms</subject><isbn>9781467321808</isbn><isbn>146732180X</isbn><isbn>9781467321815</isbn><isbn>1467321818</isbn><isbn>9781467321792</isbn><isbn>1467321796</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2012</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNp10EFLwzAUB_CICMrcF9BLP4CdeU3SJngqm7rBwIPVa3jJ0i1S29E0yPz0ViaCB09_3uP33uFPyBXQGQBVt4vVvCpnGYVslnOAgokTMlWFBJ4XLAMJ4vTPTOU5mYbwRikd73OZsQtyV7bYHD59u02qaGKDfVL5EKJLfJssfRi6PQ67rum23mKTVLtx_ezs4Ls2XJKzGpvgpj85IS8P99V8ma6fHlfzcp36TPEhZRsmauEyq5Thea4kR0URRG1qRpXKwW6sNFhspOVYcOCW104IqBGokRLZhKTHv-HD7aPR-96_Y3_QHXodmmiw_w4dnB5bUDD6m3_9wr-Wuuu3OkYNUgouR3595N4594t_GmVfOHpqWw</recordid><startdate>201212</startdate><enddate>201212</enddate><creator>Fakhrzadeh, A.</creator><creator>Spörndly-Nees, E.</creator><creator>Holm, L.</creator><creator>Hendriks, C. 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L.</creatorcontrib><creatorcontrib>Sveriges lantbruksuniversitet</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 Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection><collection>SwePub</collection><collection>SwePub Conference</collection><collection>SWEPUB Uppsala universitet</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Fakhrzadeh, A.</au><au>Spörndly-Nees, E.</au><au>Holm, L.</au><au>Hendriks, C. L. L.</au><aucorp>Sveriges lantbruksuniversitet</aucorp><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Analyzing Tubular Tissue in Histopathological Thin Sections</atitle><btitle>2012 INTERNATIONAL CONFERENCE ON DIGITAL IMAGE COMPUTING TECHNIQUES AND APPLICATIONS (DICTA)</btitle><stitle>DICTA</stitle><date>2012-12</date><risdate>2012</risdate><spage>1</spage><epage>6</epage><pages>1-6</pages><isbn>9781467321808</isbn><isbn>146732180X</isbn><eisbn>9781467321815</eisbn><eisbn>1467321818</eisbn><eisbn>9781467321792</eisbn><eisbn>1467321796</eisbn><abstract>We propose a method for automatic segmentation of tubules in the stained thin sections of various tissue types. Tubules consist of one or more layers of cells surrounding a cavity. The segmented tubules can be used to study the morphology of the tissue. Some research has been done to automatically estimate the density of tubules. To the best of our knowledge, no one has been able to, fully automatically, segment the whole tubule. Usually the border between tubules is subtle and appears broken in a straight-forward segmentation. Here we suggest delineating these borders using the geodesic distance transform. We apply this method on images of Periodic Acid Shiffs (PAS) stained thin sections of testicular tissue, delineating 89% of the tubules correctly.</abstract><pub>IEEE</pub><doi>10.1109/DICTA.2012.6411735</doi><tpages>6</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Glands Image color analysis Image edge detection Image segmentation Level set Medical Image Processing Medicinsk bildbehandling Morphology Transforms |
title | Analyzing Tubular Tissue in Histopathological Thin Sections |
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