Segmentation of liver and spleen based on computational anatomy models
Abstract Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabi...
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description | Abstract Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906 mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992 mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs ( p < 0.00001 ). |
doi_str_mv | 10.1016/j.compbiomed.2015.10.007 |
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Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906 mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992 mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs ( p < 0.00001 ).</description><identifier>ISSN: 0010-4825</identifier><identifier>EISSN: 1879-0534</identifier><identifier>DOI: 10.1016/j.compbiomed.2015.10.007</identifier><identifier>PMID: 26551453</identifier><identifier>CODEN: CBMDAW</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Abdomen ; Adult ; Aged ; Algorithms ; Automation ; Computational anatomy model ; Computer Simulation ; Construction ; Female ; Humans ; Imaging, Three-Dimensional - methods ; Internal Medicine ; Iterative probabilistic atlas ; Liver ; Liver - diagnostic imaging ; Male ; Methods ; Middle Aged ; Models, Anatomic ; Models, Biological ; Models, Statistical ; Multiple organs segmentation ; Organ bounding box ; Other ; Pattern Recognition, Automated - methods ; Radiographic Image Enhancement - methods ; Radiographic Image Interpretation, Computer-Assisted - methods ; Radiography, Abdominal - methods ; Registration ; Reproducibility of Results ; Sensitivity and Specificity ; Spleen ; Spleen - diagnostic imaging ; Subtraction Technique ; Template matching ; Tomography, X-Ray Computed - methods</subject><ispartof>Computers in biology and medicine, 2015-12, Vol.67, p.146-160</ispartof><rights>Elsevier Ltd</rights><rights>2015 Elsevier Ltd</rights><rights>Copyright © 2015 Elsevier Ltd. All rights reserved.</rights><rights>Copyright Elsevier Limited Dec 2015</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c556t-b324160e480350920522d62b0197e9b75aca61ced4f204cf85d2ed445580696d3</citedby><cites>FETCH-LOGICAL-c556t-b324160e480350920522d62b0197e9b75aca61ced4f204cf85d2ed445580696d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.proquest.com/docview/1738050446?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,3550,27924,27925,45995,64385,64387,64389,72469</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26551453$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dong, Chunhua</creatorcontrib><creatorcontrib>Chen, Yen-wei</creatorcontrib><creatorcontrib>Foruzan, Amir Hossein</creatorcontrib><creatorcontrib>Lin, Lanfen</creatorcontrib><creatorcontrib>Han, Xian-hua</creatorcontrib><creatorcontrib>Tateyama, Tomoko</creatorcontrib><creatorcontrib>Wu, Xing</creatorcontrib><creatorcontrib>Xu, Gang</creatorcontrib><creatorcontrib>Jiang, Huiyan</creatorcontrib><title>Segmentation of liver and spleen based on computational anatomy models</title><title>Computers in biology and medicine</title><addtitle>Comput Biol Med</addtitle><description>Abstract Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906 mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992 mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs ( p < 0.00001 ).</description><subject>Abdomen</subject><subject>Adult</subject><subject>Aged</subject><subject>Algorithms</subject><subject>Automation</subject><subject>Computational anatomy model</subject><subject>Computer Simulation</subject><subject>Construction</subject><subject>Female</subject><subject>Humans</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Internal Medicine</subject><subject>Iterative probabilistic atlas</subject><subject>Liver</subject><subject>Liver - diagnostic imaging</subject><subject>Male</subject><subject>Methods</subject><subject>Middle Aged</subject><subject>Models, Anatomic</subject><subject>Models, Biological</subject><subject>Models, Statistical</subject><subject>Multiple organs segmentation</subject><subject>Organ bounding box</subject><subject>Other</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Radiographic Image Enhancement - methods</subject><subject>Radiographic Image Interpretation, Computer-Assisted - methods</subject><subject>Radiography, Abdominal - methods</subject><subject>Registration</subject><subject>Reproducibility of Results</subject><subject>Sensitivity and Specificity</subject><subject>Spleen</subject><subject>Spleen - diagnostic imaging</subject><subject>Subtraction Technique</subject><subject>Template matching</subject><subject>Tomography, X-Ray Computed - methods</subject><issn>0010-4825</issn><issn>1879-0534</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkk1r3DAQhkVpaLZp_0Ix9NKLN6NP25dCG5o0EMgh7VnI0rhoK1tbyQ7sv6-cTQjklJMYzTPzMvMOIRWFLQWqzndbG8d97-OIbsuAyvK9BWjekA1tm64GycVbsgGgUIuWyVPyPucdAAjg8I6cMiUlFZJvyOUd_hlxms3s41TFoQr-HlNlJlflfUCcqt5kdFVJrpLLETShEGaO46Eao8OQP5CTwYSMHx_fM_L78sevi5_1ze3V9cW3m9pKqea650xQBSha4BI6BpIxp1gPtGuw6xtprFHUohMDA2GHVjpWAiFlC6pTjp-RL8e--xT_LZhnPfpsMQQzYVyypo1ivKG8Ua9AuRKUdx0v6OcX6C4uqUz5QLUgQYi1YXukbIo5Jxz0PvnRpIOmoFdb9E4_26JXW9ZMsaWUfnoUWPo191T45EMBvh-Bsku895h0th6nsgmf0M7aRf8ala8vmtjgJ29N-IsHzM8z6cw06Lv1PNbroBKAC9ry_y3htaU</recordid><startdate>20151201</startdate><enddate>20151201</enddate><creator>Dong, Chunhua</creator><creator>Chen, Yen-wei</creator><creator>Foruzan, Amir Hossein</creator><creator>Lin, Lanfen</creator><creator>Han, Xian-hua</creator><creator>Tateyama, Tomoko</creator><creator>Wu, Xing</creator><creator>Xu, Gang</creator><creator>Jiang, Huiyan</creator><general>Elsevier Ltd</general><general>Elsevier Limited</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7RV</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>8G5</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>GUQSH</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>KB0</scope><scope>LK8</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M2O</scope><scope>M7P</scope><scope>M7Z</scope><scope>MBDVC</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope><scope>7X8</scope><scope>7QO</scope></search><sort><creationdate>20151201</creationdate><title>Segmentation of liver and spleen based on computational anatomy models</title><author>Dong, Chunhua ; Chen, Yen-wei ; Foruzan, Amir Hossein ; Lin, Lanfen ; Han, Xian-hua ; Tateyama, Tomoko ; Wu, Xing ; Xu, Gang ; Jiang, Huiyan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c556t-b324160e480350920522d62b0197e9b75aca61ced4f204cf85d2ed445580696d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Abdomen</topic><topic>Adult</topic><topic>Aged</topic><topic>Algorithms</topic><topic>Automation</topic><topic>Computational anatomy model</topic><topic>Computer Simulation</topic><topic>Construction</topic><topic>Female</topic><topic>Humans</topic><topic>Imaging, Three-Dimensional - methods</topic><topic>Internal Medicine</topic><topic>Iterative probabilistic atlas</topic><topic>Liver</topic><topic>Liver - diagnostic imaging</topic><topic>Male</topic><topic>Methods</topic><topic>Middle Aged</topic><topic>Models, Anatomic</topic><topic>Models, Biological</topic><topic>Models, Statistical</topic><topic>Multiple organs segmentation</topic><topic>Organ bounding box</topic><topic>Other</topic><topic>Pattern Recognition, Automated - methods</topic><topic>Radiographic Image Enhancement - methods</topic><topic>Radiographic Image Interpretation, Computer-Assisted - methods</topic><topic>Radiography, Abdominal - methods</topic><topic>Registration</topic><topic>Reproducibility of Results</topic><topic>Sensitivity and Specificity</topic><topic>Spleen</topic><topic>Spleen - diagnostic imaging</topic><topic>Subtraction Technique</topic><topic>Template matching</topic><topic>Tomography, X-Ray Computed - methods</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dong, Chunhua</creatorcontrib><creatorcontrib>Chen, Yen-wei</creatorcontrib><creatorcontrib>Foruzan, Amir Hossein</creatorcontrib><creatorcontrib>Lin, Lanfen</creatorcontrib><creatorcontrib>Han, Xian-hua</creatorcontrib><creatorcontrib>Tateyama, Tomoko</creatorcontrib><creatorcontrib>Wu, Xing</creatorcontrib><creatorcontrib>Xu, Gang</creatorcontrib><creatorcontrib>Jiang, Huiyan</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Research Library (Alumni Edition)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>Research Library Prep</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>ProQuest Biological Science Collection</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Research Library</collection><collection>Biological Science Database</collection><collection>Biochemistry Abstracts 1</collection><collection>Research Library (Corporate)</collection><collection>Nursing & Allied Health Premium</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>Biotechnology Research Abstracts</collection><jtitle>Computers in biology and medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dong, Chunhua</au><au>Chen, Yen-wei</au><au>Foruzan, Amir Hossein</au><au>Lin, Lanfen</au><au>Han, Xian-hua</au><au>Tateyama, Tomoko</au><au>Wu, Xing</au><au>Xu, Gang</au><au>Jiang, Huiyan</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Segmentation of liver and spleen based on computational anatomy models</atitle><jtitle>Computers in biology and medicine</jtitle><addtitle>Comput Biol Med</addtitle><date>2015-12-01</date><risdate>2015</risdate><volume>67</volume><spage>146</spage><epage>160</epage><pages>146-160</pages><issn>0010-4825</issn><eissn>1879-0534</eissn><coden>CBMDAW</coden><abstract>Abstract Accurate segmentation of abdominal organs is a key step in developing a computer-aided diagnosis (CAD) system. Probabilistic atlas based on human anatomical structure, used as a priori information in a Bayes framework, has been widely used for organ segmentation. How to register the probabilistic atlas to the patient volume is the main challenge. Additionally, there is the disadvantage that the conventional probabilistic atlas may cause a bias toward the specific patient study because of the single reference. Taking these into consideration, a template matching framework based on an iterative probabilistic atlas for liver and spleen segmentation is presented in this paper. First, a bounding box based on human anatomical localization, which refers to the statistical geometric location of the organ, is detected for the candidate organ. Then, the probabilistic atlas is used as a template to find the organ in this bounding box by using template matching technology. We applied our method to 60 datasets including normal and pathological cases. For the liver, the Dice/Tanimoto volume overlaps were 0.930/0.870, the root-mean-squared error (RMSE) was 2.906 mm. For the spleen, quantification led to 0.922 Dice/0.857 Tanimoto overlaps, 1.992 mm RMSE. The algorithm is robust in segmenting normal and abnormal spleens and livers, such as the presence of tumors and large morphological changes. Comparing our method with conventional and recently developed atlas-based methods, our results show an improvement in the segmentation accuracy for multi-organs ( p < 0.00001 ).</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>26551453</pmid><doi>10.1016/j.compbiomed.2015.10.007</doi><tpages>15</tpages></addata></record> |
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subjects | Abdomen Adult Aged Algorithms Automation Computational anatomy model Computer Simulation Construction Female Humans Imaging, Three-Dimensional - methods Internal Medicine Iterative probabilistic atlas Liver Liver - diagnostic imaging Male Methods Middle Aged Models, Anatomic Models, Biological Models, Statistical Multiple organs segmentation Organ bounding box Other Pattern Recognition, Automated - methods Radiographic Image Enhancement - methods Radiographic Image Interpretation, Computer-Assisted - methods Radiography, Abdominal - methods Registration Reproducibility of Results Sensitivity and Specificity Spleen Spleen - diagnostic imaging Subtraction Technique Template matching Tomography, X-Ray Computed - methods |
title | Segmentation of liver and spleen based on computational anatomy models |
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