Stochastic process for white matter injury detection in preterm neonates
Preterm births are rising in Canada and worldwide. As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required. Infants at highest risk will be able to receive early developmental interventions, and will al...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015-01-01
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| Series: | NeuroImage: Clinical |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158215000339 |
| _version_ | 1828543844976689152 |
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| author | Irene Cheng Steven P. Miller Emma G. Duerden Kaiyu Sun Vann Chau Elysia Adams Kenneth J. Poskitt Helen M. Branson Anup Basu |
| author_facet | Irene Cheng Steven P. Miller Emma G. Duerden Kaiyu Sun Vann Chau Elysia Adams Kenneth J. Poskitt Helen M. Branson Anup Basu |
| author_sort | Irene Cheng |
| collection | DOAJ |
| description | Preterm births are rising in Canada and worldwide. As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required. Infants at highest risk will be able to receive early developmental interventions, and will also enable clinicians to implement and evaluate new methods to improve outcomes. While severe white matter injury (WMI) is associated with adverse developmental outcome, more subtle injuries are difficult to identify and the association with later impairments remains unknown. Thus, our goal was to develop an automated method for detection and visualization of brain abnormalities in MR images acquired in very preterm born neonates. We have developed a technique to detect WMI in T1-weighted images acquired in 177 very preterm born infants (24–32 weeks gestation). Our approach uses a stochastic process that estimates the likelihood of intensity variations in nearby pixels; with small variations being more likely than large variations. We first detect the boundaries between normal and injured regions of the white matter. Following this we use a measure of pixel similarity to identify WMI regions. Our algorithm is able to detect WMI in all of the images in the ground truth dataset with some false positives in situations where the white matter region is not segmented accurately. |
| first_indexed | 2024-12-12T02:20:59Z |
| format | Article |
| id | doaj.art-fd385853d7aa4e27ab253c2b73e78e07 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-12T02:20:59Z |
| publishDate | 2015-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-fd385853d7aa4e27ab253c2b73e78e072022-12-22T00:41:41ZengElsevierNeuroImage: Clinical2213-15822015-01-017C62263010.1016/j.nicl.2015.02.015Stochastic process for white matter injury detection in preterm neonatesIrene Cheng0Steven P. Miller1Emma G. Duerden2Kaiyu Sun3Vann Chau4Elysia Adams5Kenneth J. Poskitt6Helen M. Branson7Anup Basu8Department of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, CanadaHospital for Sick Children and the University of Toronto, Toronto, CanadaHospital for Sick Children and the University of Toronto, Toronto, CanadaDepartment of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, CanadaHospital for Sick Children and the University of Toronto, Toronto, CanadaHospital for Sick Children and the University of Toronto, Toronto, CanadaBC Children's Hospital and the University of British Columbia, Vancouver, CanadaHospital for Sick Children and the University of Toronto, Toronto, CanadaDepartment of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, CanadaPreterm births are rising in Canada and worldwide. As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required. Infants at highest risk will be able to receive early developmental interventions, and will also enable clinicians to implement and evaluate new methods to improve outcomes. While severe white matter injury (WMI) is associated with adverse developmental outcome, more subtle injuries are difficult to identify and the association with later impairments remains unknown. Thus, our goal was to develop an automated method for detection and visualization of brain abnormalities in MR images acquired in very preterm born neonates. We have developed a technique to detect WMI in T1-weighted images acquired in 177 very preterm born infants (24–32 weeks gestation). Our approach uses a stochastic process that estimates the likelihood of intensity variations in nearby pixels; with small variations being more likely than large variations. We first detect the boundaries between normal and injured regions of the white matter. Following this we use a measure of pixel similarity to identify WMI regions. Our algorithm is able to detect WMI in all of the images in the ground truth dataset with some false positives in situations where the white matter region is not segmented accurately.http://www.sciencedirect.com/science/article/pii/S2213158215000339White matter injuryPreterm neonatesStochastic process |
| spellingShingle | Irene Cheng Steven P. Miller Emma G. Duerden Kaiyu Sun Vann Chau Elysia Adams Kenneth J. Poskitt Helen M. Branson Anup Basu Stochastic process for white matter injury detection in preterm neonates NeuroImage: Clinical White matter injury Preterm neonates Stochastic process |
| title | Stochastic process for white matter injury detection in preterm neonates |
| title_full | Stochastic process for white matter injury detection in preterm neonates |
| title_fullStr | Stochastic process for white matter injury detection in preterm neonates |
| title_full_unstemmed | Stochastic process for white matter injury detection in preterm neonates |
| title_short | Stochastic process for white matter injury detection in preterm neonates |
| title_sort | stochastic process for white matter injury detection in preterm neonates |
| topic | White matter injury Preterm neonates Stochastic process |
| url | http://www.sciencedirect.com/science/article/pii/S2213158215000339 |
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