An enhanced random walk algorithm for delineation of head and neck cancers in PET studies
An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesi...
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Veröffentlicht in: | Medical & biological engineering & computing 2017-06, Vol.55 (6), p.897-908 |
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creator | Stefano, Alessandro Vitabile, Salvatore Russo, Giorgio Ippolito, Massimo Sabini, Maria Gabriella Sardina, Daniele Gambino, Orazio Pirrone, Roberto Ardizzone, Edoardo Gilardi, Maria Carla |
description | An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment. |
doi_str_mv | 10.1007/s11517-016-1571-0 |
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An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-016-1571-0</identifier><identifier>PMID: 27638108</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Bifurcations ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; Cancer ; Clustering ; Computer Applications ; Delineation ; Feasibility studies ; Head & neck cancer ; Head and neck ; Head and Neck Neoplasms - diagnosis ; Human Physiology ; Humans ; Image enhancement ; Image processing ; Image Processing, Computer-Assisted - methods ; Image segmentation ; Imaging ; Lesions ; Original Article ; Phantoms, Imaging ; Positron emission ; Positron emission tomography ; Positron-Emission Tomography - methods ; Radiation therapy ; Radiology ; Random walk ; Real time ; Similarity ; Tomography</subject><ispartof>Medical & biological engineering & computing, 2017-06, Vol.55 (6), p.897-908</ispartof><rights>International Federation for Medical and Biological Engineering 2016</rights><rights>Medical & Biological Engineering & Computing is a copyright of Springer, 2017.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c415t-25ecb654c4ed227878d8de1320bfe4082806120d44e3eafee3989128e5769b553</citedby><cites>FETCH-LOGICAL-c415t-25ecb654c4ed227878d8de1320bfe4082806120d44e3eafee3989128e5769b553</cites><orcidid>0000-0002-7189-1731</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-016-1571-0$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-016-1571-0$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27638108$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Stefano, Alessandro</creatorcontrib><creatorcontrib>Vitabile, Salvatore</creatorcontrib><creatorcontrib>Russo, Giorgio</creatorcontrib><creatorcontrib>Ippolito, Massimo</creatorcontrib><creatorcontrib>Sabini, Maria Gabriella</creatorcontrib><creatorcontrib>Sardina, Daniele</creatorcontrib><creatorcontrib>Gambino, Orazio</creatorcontrib><creatorcontrib>Pirrone, Roberto</creatorcontrib><creatorcontrib>Ardizzone, Edoardo</creatorcontrib><creatorcontrib>Gilardi, Maria Carla</creatorcontrib><title>An enhanced random walk algorithm for delineation of head and neck cancers in PET studies</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.</description><subject>Algorithms</subject><subject>Bifurcations</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Cancer</subject><subject>Clustering</subject><subject>Computer Applications</subject><subject>Delineation</subject><subject>Feasibility studies</subject><subject>Head & neck cancer</subject><subject>Head and neck</subject><subject>Head and Neck Neoplasms - diagnosis</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Image enhancement</subject><subject>Image processing</subject><subject>Image Processing, Computer-Assisted - methods</subject><subject>Image segmentation</subject><subject>Imaging</subject><subject>Lesions</subject><subject>Original Article</subject><subject>Phantoms, 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enhanced random walk algorithm for delineation of head and neck cancers in PET studies</title><author>Stefano, Alessandro ; Vitabile, Salvatore ; Russo, Giorgio ; Ippolito, Massimo ; Sabini, Maria Gabriella ; Sardina, Daniele ; Gambino, Orazio ; Pirrone, Roberto ; Ardizzone, Edoardo ; Gilardi, Maria Carla</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c415t-25ecb654c4ed227878d8de1320bfe4082806120d44e3eafee3989128e5769b553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>Bifurcations</topic><topic>Biomedical and Life Sciences</topic><topic>Biomedical Engineering and Bioengineering</topic><topic>Biomedicine</topic><topic>Cancer</topic><topic>Clustering</topic><topic>Computer Applications</topic><topic>Delineation</topic><topic>Feasibility studies</topic><topic>Head & neck cancer</topic><topic>Head and neck</topic><topic>Head and Neck Neoplasms - diagnosis</topic><topic>Human Physiology</topic><topic>Humans</topic><topic>Image enhancement</topic><topic>Image processing</topic><topic>Image Processing, Computer-Assisted - methods</topic><topic>Image segmentation</topic><topic>Imaging</topic><topic>Lesions</topic><topic>Original Article</topic><topic>Phantoms, Imaging</topic><topic>Positron emission</topic><topic>Positron emission tomography</topic><topic>Positron-Emission Tomography - methods</topic><topic>Radiation therapy</topic><topic>Radiology</topic><topic>Random walk</topic><topic>Real time</topic><topic>Similarity</topic><topic>Tomography</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Stefano, Alessandro</creatorcontrib><creatorcontrib>Vitabile, Salvatore</creatorcontrib><creatorcontrib>Russo, Giorgio</creatorcontrib><creatorcontrib>Ippolito, Massimo</creatorcontrib><creatorcontrib>Sabini, Maria Gabriella</creatorcontrib><creatorcontrib>Sardina, 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Carla</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An enhanced random walk algorithm for delineation of head and neck cancers in PET studies</atitle><jtitle>Medical & biological engineering & computing</jtitle><stitle>Med Biol Eng Comput</stitle><addtitle>Med Biol Eng Comput</addtitle><date>2017-06-01</date><risdate>2017</risdate><volume>55</volume><issue>6</issue><spage>897</spage><epage>908</epage><pages>897-908</pages><issn>0140-0118</issn><eissn>1741-0444</eissn><abstract>An algorithm for delineating complex head and neck cancers in positron emission tomography (PET) images is presented in this article. An enhanced random walk (RW) algorithm with automatic seed detection is proposed and used to make the segmentation process feasible in the event of inhomogeneous lesions with bifurcations. In addition, an adaptive probability threshold and a k-means based clustering technique have been integrated in the proposed enhanced RW algorithm. The new threshold is capable of following the intensity changes between adjacent slices along the whole cancer volume, leading to an operator-independent algorithm. Validation experiments were first conducted on phantom studies: High Dice similarity coefficients, high true positive volume fractions, and low Hausdorff distance confirm the accuracy of the proposed method. Subsequently, forty head and neck lesions were segmented in order to evaluate the clinical feasibility of the proposed approach against the most common segmentation algorithms. Experimental results show that the proposed algorithm is more accurate and robust than the most common algorithms in the literature. Finally, the proposed method also shows real-time performance, addressing the physician’s requirements in a radiotherapy environment.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer Berlin Heidelberg</pub><pmid>27638108</pmid><doi>10.1007/s11517-016-1571-0</doi><tpages>12</tpages><orcidid>https://orcid.org/0000-0002-7189-1731</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Bifurcations Biomedical and Life Sciences Biomedical Engineering and Bioengineering Biomedicine Cancer Clustering Computer Applications Delineation Feasibility studies Head & neck cancer Head and neck Head and Neck Neoplasms - diagnosis Human Physiology Humans Image enhancement Image processing Image Processing, Computer-Assisted - methods Image segmentation Imaging Lesions Original Article Phantoms, Imaging Positron emission Positron emission tomography Positron-Emission Tomography - methods Radiation therapy Radiology Random walk Real time Similarity Tomography |
title | An enhanced random walk algorithm for delineation of head and neck cancers in PET studies |
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