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Nonlocal intracranial cavity extraction.

Manjón JV, Eskildsen SF, Coupé P, Romero JE, Collins DL, Robles M - Int J Biomed Imaging (2014)

Bottom Line: However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging.To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation.The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

ABSTRACT
Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

No MeSH data available.


Related in: MedlinePlus

False positive and false negative maps for NICE, BEaST, and VBM8 on SVE dataset. VBM8 tended to produce a systematic oversegmentation compared to the used manual gold standard. The errors obtained by NICE and BEaST were more uniformly distributed indicating nonsystematic segmentation errors. Note that in the images provided by the SVE website the vertical scale measuring error is not the same over all images.
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fig7: False positive and false negative maps for NICE, BEaST, and VBM8 on SVE dataset. VBM8 tended to produce a systematic oversegmentation compared to the used manual gold standard. The errors obtained by NICE and BEaST were more uniformly distributed indicating nonsystematic segmentation errors. Note that in the images provided by the SVE website the vertical scale measuring error is not the same over all images.

Mentions: Validation of NICE using the SVE test dataset resulted in a mean DICE of 0.9819 ± 0.0024 (see http://sve.bmap.ucla.edu/archivel/). At the time of writing, this result was the best (P < 0.01) of all the methods published on the website. BEaST had a DSC of 0.9781 ± 0.0047 and VBM8 obtained a DSC of 0.9760 ± 0.0025. Sensitivity and specificity results are also included in Table 3. A visual representation of false positive and false negative as supplied by the website is presented at Figure 7.


Nonlocal intracranial cavity extraction.

Manjón JV, Eskildsen SF, Coupé P, Romero JE, Collins DL, Robles M - Int J Biomed Imaging (2014)

False positive and false negative maps for NICE, BEaST, and VBM8 on SVE dataset. VBM8 tended to produce a systematic oversegmentation compared to the used manual gold standard. The errors obtained by NICE and BEaST were more uniformly distributed indicating nonsystematic segmentation errors. Note that in the images provided by the SVE website the vertical scale measuring error is not the same over all images.
© Copyright Policy - open-access
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4195262&req=5

fig7: False positive and false negative maps for NICE, BEaST, and VBM8 on SVE dataset. VBM8 tended to produce a systematic oversegmentation compared to the used manual gold standard. The errors obtained by NICE and BEaST were more uniformly distributed indicating nonsystematic segmentation errors. Note that in the images provided by the SVE website the vertical scale measuring error is not the same over all images.
Mentions: Validation of NICE using the SVE test dataset resulted in a mean DICE of 0.9819 ± 0.0024 (see http://sve.bmap.ucla.edu/archivel/). At the time of writing, this result was the best (P < 0.01) of all the methods published on the website. BEaST had a DSC of 0.9781 ± 0.0047 and VBM8 obtained a DSC of 0.9760 ± 0.0025. Sensitivity and specificity results are also included in Table 3. A visual representation of false positive and false negative as supplied by the website is presented at Figure 7.

Bottom Line: However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging.To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation.The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

View Article: PubMed Central - PubMed

Affiliation: Instituto de Aplicaciones de las Tecnologías de la Información y de las Comunicaciones Avanzadas (ITACA), Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.

ABSTRACT
Automatic and accurate methods to estimate normalized regional brain volumes from MRI data are valuable tools which may help to obtain an objective diagnosis and followup of many neurological diseases. To estimate such regional brain volumes, the intracranial cavity volume (ICV) is often used for normalization. However, the high variability of brain shape and size due to normal intersubject variability, normal changes occurring over the lifespan, and abnormal changes due to disease makes the ICV estimation problem challenging. In this paper, we present a new approach to perform ICV extraction based on the use of a library of prelabeled brain images to capture the large variability of brain shapes. To this end, an improved nonlocal label fusion scheme based on BEaST technique is proposed to increase the accuracy of the ICV estimation. The proposed method is compared with recent state-of-the-art methods and the results demonstrate an improved performance both in terms of accuracy and reproducibility while maintaining a reduced computational burden.

No MeSH data available.


Related in: MedlinePlus