Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model
Previous work has shown that the segmentation of anatomical structures on 3D ultrasound data sets provides an important tool for the assessment of the fetal health. In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. T...
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| Veröffentlicht in: | Medical & biological engineering & computing 2013-09, Vol.51 (9), p.1021-1030 |
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| creator | Gutierrez-Becker, Benjamin Arambula Cosio, Fernando Guzman Huerta, Mario E Benavides-Serralde, Jesus Andres Camargo-Marin, Lisbeth Medina Banuelos, Veronica |
| description | Previous work has shown that the segmentation of anatomical structures on 3D ultrasound data sets provides an important tool for the assessment of the fetal health. In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. This model is adjusted using an ad hoc objective function which is in turn optimized using the Nelder–Mead simplex algorithm. Our algorithm was tested on ultrasound volumes of the fetal brain taken from 20 pregnant women, between 18 and 24 gestational weeks. An intraclass correlation coefficient of 0.8528 and a mean Dice coefficient of 0.8 between cerebellar volumes measured using manual techniques and the volumes calculated using our algorithm were obtained. As far as we know, this is the first effort to automatically segment fetal intracranial structures on 3D ultrasound data. |
| doi_str_mv | 10.1007/s11517-013-1082-1 |
| format | Article |
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In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. This model is adjusted using an ad hoc objective function which is in turn optimized using the Nelder–Mead simplex algorithm. Our algorithm was tested on ultrasound volumes of the fetal brain taken from 20 pregnant women, between 18 and 24 gestational weeks. An intraclass correlation coefficient of 0.8528 and a mean Dice coefficient of 0.8 between cerebellar volumes measured using manual techniques and the volumes calculated using our algorithm were obtained. As far as we know, this is the first effort to automatically segment fetal intracranial structures on 3D ultrasound data.</description><identifier>ISSN: 0140-0118</identifier><identifier>EISSN: 1741-0444</identifier><identifier>DOI: 10.1007/s11517-013-1082-1</identifier><identifier>PMID: 23686392</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer Berlin Heidelberg</publisher><subject>Algorithms ; Biomedical and Life Sciences ; Biomedical engineering ; Biomedical Engineering and Bioengineering ; Biomedicine ; Brain research ; Cerebellum - diagnostic imaging ; Cerebellum - embryology ; Child development ; Computer Applications ; Echoencephalography - methods ; Female ; Fetuses ; Gestational age ; Human Physiology ; Humans ; Imaging ; Imaging, Three-Dimensional - methods ; Magnetic resonance imaging ; Medicine ; Models, Statistical ; Original Article ; Pregnancy ; Radiology ; Reproducibility of Results ; Studies ; Ultrasonic imaging ; Ultrasonography, Prenatal - methods</subject><ispartof>Medical & biological engineering & computing, 2013-09, Vol.51 (9), p.1021-1030</ispartof><rights>International Federation for Medical and Biological Engineering 2013</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c471t-eb999858374a736a82d3387581b2cce53e9b9351d5b674a6dddc2e2c710ce5b63</citedby><cites>FETCH-LOGICAL-c471t-eb999858374a736a82d3387581b2cce53e9b9351d5b674a6dddc2e2c710ce5b63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s11517-013-1082-1$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s11517-013-1082-1$$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/23686392$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gutierrez-Becker, Benjamin</creatorcontrib><creatorcontrib>Arambula Cosio, Fernando</creatorcontrib><creatorcontrib>Guzman Huerta, Mario E</creatorcontrib><creatorcontrib>Benavides-Serralde, Jesus Andres</creatorcontrib><creatorcontrib>Camargo-Marin, Lisbeth</creatorcontrib><creatorcontrib>Medina Banuelos, Veronica</creatorcontrib><title>Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model</title><title>Medical & biological engineering & computing</title><addtitle>Med Biol Eng Comput</addtitle><addtitle>Med Biol Eng Comput</addtitle><description>Previous work has shown that the segmentation of anatomical structures on 3D ultrasound data sets provides an important tool for the assessment of the fetal health. In this work, we present an algorithm based on a 3D statistical shape model to segment the fetal cerebellum on 3D ultrasound volumes. This model is adjusted using an ad hoc objective function which is in turn optimized using the Nelder–Mead simplex algorithm. Our algorithm was tested on ultrasound volumes of the fetal brain taken from 20 pregnant women, between 18 and 24 gestational weeks. An intraclass correlation coefficient of 0.8528 and a mean Dice coefficient of 0.8 between cerebellar volumes measured using manual techniques and the volumes calculated using our algorithm were obtained. As far as we know, this is the first effort to automatically segment fetal intracranial structures on 3D ultrasound data.</description><subject>Algorithms</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedical engineering</subject><subject>Biomedical Engineering and Bioengineering</subject><subject>Biomedicine</subject><subject>Brain research</subject><subject>Cerebellum - diagnostic imaging</subject><subject>Cerebellum - embryology</subject><subject>Child development</subject><subject>Computer Applications</subject><subject>Echoencephalography - methods</subject><subject>Female</subject><subject>Fetuses</subject><subject>Gestational age</subject><subject>Human Physiology</subject><subject>Humans</subject><subject>Imaging</subject><subject>Imaging, Three-Dimensional - methods</subject><subject>Magnetic resonance imaging</subject><subject>Medicine</subject><subject>Models, Statistical</subject><subject>Original 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| subjects | Algorithms Biomedical and Life Sciences Biomedical engineering Biomedical Engineering and Bioengineering Biomedicine Brain research Cerebellum - diagnostic imaging Cerebellum - embryology Child development Computer Applications Echoencephalography - methods Female Fetuses Gestational age Human Physiology Humans Imaging Imaging, Three-Dimensional - methods Magnetic resonance imaging Medicine Models, Statistical Original Article Pregnancy Radiology Reproducibility of Results Studies Ultrasonic imaging Ultrasonography, Prenatal - methods |
| title | Automatic segmentation of the fetal cerebellum on ultrasound volumes, using a 3D statistical shape model |
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