An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy
The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of c...
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| Veröffentlicht in: | IEEE transactions on medical imaging 2005-08, Vol.24 (8), p.1011-1024 |
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| creator | Eden, E. Waisman, D. Rudzsky, M. Bitterman, H. Brod, V. Rivlin, E. |
| description | The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos. |
| doi_str_mv | 10.1109/TMI.2005.851759 |
| format | Article |
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Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos.</description><identifier>ISSN: 0278-0062</identifier><identifier>EISSN: 1558-254X</identifier><identifier>DOI: 10.1109/TMI.2005.851759</identifier><identifier>PMID: 16092333</identifier><identifier>CODEN: ITMID4</identifier><language>eng</language><publisher>United States: IEEE</publisher><subject>Algorithms ; Artificial Intelligence ; Behavior ; Blood ; Blood cells ; Cells (biology) ; Color ; Computer Systems ; Data mining ; Flow Cytometry - methods ; Humans ; Image Enhancement - methods ; Image Interpretation, Computer-Assisted - methods ; In vivo ; Information Storage and Retrieval - methods ; leukocyte ; Leukocytes - cytology ; Leukocytes - physiology ; microcirculation ; Microcirculation - cytology ; Microscopy ; Microscopy, Video - methods ; motion correspondence ; Neural networks ; Particle Size ; Pattern analysis ; Pattern Recognition, Automated - methods ; Physiology ; Red blood cells ; Reproducibility of Results ; segmentation ; Sensitivity and Specificity ; Shape ; Studies ; tracking ; Velocity measurement</subject><ispartof>IEEE transactions on medical imaging, 2005-08, Vol.24 (8), p.1011-1024</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2005</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-d7b2bd6334e69964ba313278d1de267f6c37b79f0965c500591e018949b6bbbd3</citedby><cites>FETCH-LOGICAL-c414t-d7b2bd6334e69964ba313278d1de267f6c37b79f0965c500591e018949b6bbbd3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/1490670$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,27929,27930,54763</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/1490670$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/16092333$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Eden, E.</creatorcontrib><creatorcontrib>Waisman, D.</creatorcontrib><creatorcontrib>Rudzsky, M.</creatorcontrib><creatorcontrib>Bitterman, H.</creatorcontrib><creatorcontrib>Brod, V.</creatorcontrib><creatorcontrib>Rivlin, E.</creatorcontrib><title>An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy</title><title>IEEE transactions on medical imaging</title><addtitle>TMI</addtitle><addtitle>IEEE Trans Med Imaging</addtitle><description>The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos.</description><subject>Algorithms</subject><subject>Artificial Intelligence</subject><subject>Behavior</subject><subject>Blood</subject><subject>Blood cells</subject><subject>Cells (biology)</subject><subject>Color</subject><subject>Computer Systems</subject><subject>Data mining</subject><subject>Flow Cytometry - methods</subject><subject>Humans</subject><subject>Image Enhancement - methods</subject><subject>Image Interpretation, Computer-Assisted - methods</subject><subject>In vivo</subject><subject>Information Storage and Retrieval - methods</subject><subject>leukocyte</subject><subject>Leukocytes - cytology</subject><subject>Leukocytes - physiology</subject><subject>microcirculation</subject><subject>Microcirculation - cytology</subject><subject>Microscopy</subject><subject>Microscopy, Video - methods</subject><subject>motion correspondence</subject><subject>Neural networks</subject><subject>Particle Size</subject><subject>Pattern analysis</subject><subject>Pattern Recognition, Automated - methods</subject><subject>Physiology</subject><subject>Red blood cells</subject><subject>Reproducibility of Results</subject><subject>segmentation</subject><subject>Sensitivity and Specificity</subject><subject>Shape</subject><subject>Studies</subject><subject>tracking</subject><subject>Velocity measurement</subject><issn>0278-0062</issn><issn>1558-254X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2005</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><sourceid>EIF</sourceid><recordid>eNqFkUtrFEEQgBtRzBo9exCk8eBtNtXT72MIUQMRLxG8Nf0a02Fmeu2eiey_t9ddCHjxUgVVXxVUfQi9JbAlBPTF3debbQ_At4oTyfUztCGcq67n7MdztIFeqg5A9GfoVa0PAIRx0C_RGRGge0rpBuXLGdt1yZNdYsBTXO5zwEMu2M523NdUcR7wMObf2N_bYv0SS6pL8n_rPhW_jnZJ80-8s6WVx1jxUPKE04wf02NuIcSMp-RLrj7v9q_Ri8GONb455XP0_dP13dWX7vbb55ury9vOM8KWLkjXuyAoZVFoLZizlNB2TSAh9kIOwlPppB5AC-55e4AmEYjSTDvhnAv0HH087t2V_GuNdTFTqj6Oo51jXqsRignOCfsv2CugQnPdwA__gA95Le1L1ShFGZUKZIMujtDh3lriYHYlTbbsDQFzMGaaMXMwZo7G2sT709rVTTE88SdFDXh3BFKM8anNNAgJ9A_m5JqA</recordid><startdate>200508</startdate><enddate>200508</enddate><creator>Eden, E.</creator><creator>Waisman, D.</creator><creator>Rudzsky, M.</creator><creator>Bitterman, H.</creator><creator>Brod, V.</creator><creator>Rivlin, E.</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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Academic</collection><jtitle>IEEE transactions on medical imaging</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Eden, E.</au><au>Waisman, D.</au><au>Rudzsky, M.</au><au>Bitterman, H.</au><au>Brod, V.</au><au>Rivlin, E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy</atitle><jtitle>IEEE transactions on medical imaging</jtitle><stitle>TMI</stitle><addtitle>IEEE Trans Med Imaging</addtitle><date>2005-08</date><risdate>2005</risdate><volume>24</volume><issue>8</issue><spage>1011</spage><epage>1024</epage><pages>1011-1024</pages><issn>0278-0062</issn><eissn>1558-254X</eissn><coden>ITMID4</coden><abstract>The behavior of white and red blood cells, platelets, and circulating injected particles is one of the most studied areas of physiology. Most methods used to analyze the circulatory patterns of cells are time consuming. We describe a system named CellTrack, designed for fully automated tracking of circulating cells and micro-particles and retrieval of their behavioral characteristics. The task of automated blood cell tracking in vessels from in vivo video is particularly challenging because of the blood cells' nonrigid shapes, the instability inherent in in vivo videos, the abundance of moving objects and their frequent superposition. To tackle this, the CellTrack system operates on two levels: first, a global processing module extracts vessel borders and center lines based on color and temporal patterns. This enables the computation of the approximate direction of the blood flow in each vessel. Second, a local processing module extracts the locations and velocities of circulating cells. This is performed by artificial neural network classifiers that are designed to detect specific types of blood cells and micro-particles. The motion correspondence problem is then resolved by a novel algorithm that incorporates both the local and the global information. The system has been tested on a series of in vivo color video recordings of rat mesentery. Our results show that the synergy between the global and local information enables CellTrack to overcome many of the difficulties inherent in tracking methods that rely solely on local information. A comparison was made between manual measurements and the automatically extracted measurements of leukocytes and fluorescent microspheres circulatory velocities. This comparison revealed an accuracy of 97%. CellTrack also enabled a much larger volume of sampling in a fraction of time compared to the manual measurements. All these results suggest that our method can in fact constitute a reliable replacement for manual extraction of blood flow characteristics from in vivo videos.</abstract><cop>United States</cop><pub>IEEE</pub><pmid>16092333</pmid><doi>10.1109/TMI.2005.851759</doi><tpages>14</tpages><oa>free_for_read</oa></addata></record> |
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| subjects | Algorithms Artificial Intelligence Behavior Blood Blood cells Cells (biology) Color Computer Systems Data mining Flow Cytometry - methods Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods In vivo Information Storage and Retrieval - methods leukocyte Leukocytes - cytology Leukocytes - physiology microcirculation Microcirculation - cytology Microscopy Microscopy, Video - methods motion correspondence Neural networks Particle Size Pattern analysis Pattern Recognition, Automated - methods Physiology Red blood cells Reproducibility of Results segmentation Sensitivity and Specificity Shape Studies tracking Velocity measurement |
| title | An automated method for analysis of flow characteristics of circulating particles from in vivo video microscopy |
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