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Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool.

Liang Z, He X, Ceritoglu C, Tang X, Li Y, Kutten KS, Oishi K, Miller MI, Mori S, Faria AV - PLoS ONE (2015)

Bottom Line: The entire brain was parceled into five different levels of granularity.Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology.A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

View Article: PubMed Central - PubMed

Affiliation: College of Electronics and Information Engineering, Sichuan University, Chengdu, China; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.

ABSTRACT
Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

No MeSH data available.


Related in: MedlinePlus

Examples of significant correlations between regional volumes and age at granularity level 2.The colors code the correlation coefficient R in regions where the age vs. volume curve achieves significance (p-value <0.05, Bonferroni-corrected). Yellow / orange / red (R<0) are regions that shrink over time, while blue (R>0) are regions that expand over time, such as ventricles. As is shown in the figure: a. cerebral cortex (left); b. white matter (left); c. ventricle; d. thalamus (left); e. sulcus (left).
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pone.0133533.g006: Examples of significant correlations between regional volumes and age at granularity level 2.The colors code the correlation coefficient R in regions where the age vs. volume curve achieves significance (p-value <0.05, Bonferroni-corrected). Yellow / orange / red (R<0) are regions that shrink over time, while blue (R>0) are regions that expand over time, such as ventricles. As is shown in the figure: a. cerebral cortex (left); b. white matter (left); c. ventricle; d. thalamus (left); e. sulcus (left).

Mentions: As the granularity increased and the volumes of defined structures decreased, the number of areas with a significant correlation with age decreased, revealing spatial specificity of the age effect at the bilateral frontal and temporal lobes (Figs 6, 7, 8 and 9). At level 5, the white matter regions in the frontal lobes, the ventricles, and a small section in the temporal lobes reached a significant level. The analysis of the source of variation for level 5 is shown in Fig 10. Age explained 10.38% of the total variation in the data, which was much larger than the variation attributed to protocols (1.53%) and the error (1%).


Evaluation of Cross-Protocol Stability of a Fully Automated Brain Multi-Atlas Parcellation Tool.

Liang Z, He X, Ceritoglu C, Tang X, Li Y, Kutten KS, Oishi K, Miller MI, Mori S, Faria AV - PLoS ONE (2015)

Examples of significant correlations between regional volumes and age at granularity level 2.The colors code the correlation coefficient R in regions where the age vs. volume curve achieves significance (p-value <0.05, Bonferroni-corrected). Yellow / orange / red (R<0) are regions that shrink over time, while blue (R>0) are regions that expand over time, such as ventricles. As is shown in the figure: a. cerebral cortex (left); b. white matter (left); c. ventricle; d. thalamus (left); e. sulcus (left).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4514626&req=5

pone.0133533.g006: Examples of significant correlations between regional volumes and age at granularity level 2.The colors code the correlation coefficient R in regions where the age vs. volume curve achieves significance (p-value <0.05, Bonferroni-corrected). Yellow / orange / red (R<0) are regions that shrink over time, while blue (R>0) are regions that expand over time, such as ventricles. As is shown in the figure: a. cerebral cortex (left); b. white matter (left); c. ventricle; d. thalamus (left); e. sulcus (left).
Mentions: As the granularity increased and the volumes of defined structures decreased, the number of areas with a significant correlation with age decreased, revealing spatial specificity of the age effect at the bilateral frontal and temporal lobes (Figs 6, 7, 8 and 9). At level 5, the white matter regions in the frontal lobes, the ventricles, and a small section in the temporal lobes reached a significant level. The analysis of the source of variation for level 5 is shown in Fig 10. Age explained 10.38% of the total variation in the data, which was much larger than the variation attributed to protocols (1.53%) and the error (1%).

Bottom Line: The entire brain was parceled into five different levels of granularity.Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology.A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

View Article: PubMed Central - PubMed

Affiliation: College of Electronics and Information Engineering, Sichuan University, Chengdu, China; The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.

ABSTRACT
Brain parcellation tools based on multiple-atlas algorithms have recently emerged as a promising method with which to accurately define brain structures. When dealing with data from various sources, it is crucial that these tools are robust for many different imaging protocols. In this study, we tested the robustness of a multiple-atlas, likelihood fusion algorithm using Alzheimer's Disease Neuroimaging Initiative (ADNI) data with six different protocols, comprising three manufacturers and two magnetic field strengths. The entire brain was parceled into five different levels of granularity. In each level, which defines a set of brain structures, ranging from eight to 286 regions, we evaluated the variability of brain volumes related to the protocol, age, and diagnosis (healthy or Alzheimer's disease). Our results indicated that, with proper pre-processing steps, the impact of different protocols is minor compared to biological effects, such as age and pathology. A precise knowledge of the sources of data variation enables sufficient statistical power and ensures the reliability of an anatomical analysis when using this automated brain parcellation tool on datasets from various imaging protocols, such as clinical databases.

No MeSH data available.


Related in: MedlinePlus