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Stochastic process for white matter injury detection in preterm neonates.

Cheng I, Miller SP, Duerden EG, Sun K, Chau V, Adams E, Poskitt KJ, Branson HM, Basu A - Neuroimage Clin (2015)

Bottom Line: As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, Canada.

ABSTRACT
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.

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A Bland–Altman plot comparing the areas of the ground truth and automatically detected regions.
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f0040: A Bland–Altman plot comparing the areas of the ground truth and automatically detected regions.

Mentions: We show a Bland–Altman Plot in Fig. 8 comparing the areas of ground truth and automatically detected regions. This plot indicates that the automatically detected regions tend to be smaller than the corresponding manually demarked ground truth. The range of values on the vertical axis of this plot is determined by the maximum possible difference. The red line in the plot signifies the mean and has the value of 0.0942, while the blue lines correspond to ±1.96 standard deviations and have values of +0.2453 and −0.0569. From the figure we can observe that for small injury regions the difference between the ground truth area and the automatically detected area is very small. However, in general this difference tends to get larger as the size of the injury region increases. This trend can be observed in Table 1 for values below 0.5 cm2. For the x-axis value of 0.55 the difference was 0.0253, not following the trend in the table.


Stochastic process for white matter injury detection in preterm neonates.

Cheng I, Miller SP, Duerden EG, Sun K, Chau V, Adams E, Poskitt KJ, Branson HM, Basu A - Neuroimage Clin (2015)

A Bland–Altman plot comparing the areas of the ground truth and automatically detected regions.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4375636&req=5

f0040: A Bland–Altman plot comparing the areas of the ground truth and automatically detected regions.
Mentions: We show a Bland–Altman Plot in Fig. 8 comparing the areas of ground truth and automatically detected regions. This plot indicates that the automatically detected regions tend to be smaller than the corresponding manually demarked ground truth. The range of values on the vertical axis of this plot is determined by the maximum possible difference. The red line in the plot signifies the mean and has the value of 0.0942, while the blue lines correspond to ±1.96 standard deviations and have values of +0.2453 and −0.0569. From the figure we can observe that for small injury regions the difference between the ground truth area and the automatically detected area is very small. However, in general this difference tends to get larger as the size of the injury region increases. This trend can be observed in Table 1 for values below 0.5 cm2. For the x-axis value of 0.55 the difference was 0.0253, not following the trend in the table.

Bottom Line: As clinicians strive to identify preterm neonates at greatest risk of significant developmental or motor problems, accurate predictive tools are required.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.

View Article: PubMed Central - PubMed

Affiliation: Department of Computing Science, University of Alberta, Edmonton, AB T6G 2H1, Canada.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus