Limits...
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

Example segmentation results using NICE (a), BEaST (b), and VBM8 (c) methods on the three different population samples. Sagittal slices and 3D renderings of the segmentations are shown. Red voxels correspond to correct voxels in the segmentation compared to the gold standard. Blue voxels are false positives and green voxels are false negatives (AD case belongs to Oasis dataset and the adult and infant cases belong to the IXI dataset).
© Copyright Policy - open-access
Related In: Results  -  Collection


getmorefigures.php?uid=PMC4195262&req=5

fig6: Example segmentation results using NICE (a), BEaST (b), and VBM8 (c) methods on the three different population samples. Sagittal slices and 3D renderings of the segmentations are shown. Red voxels correspond to correct voxels in the segmentation compared to the gold standard. Blue voxels are false positives and green voxels are false negatives (AD case belongs to Oasis dataset and the adult and infant cases belong to the IXI dataset).

Mentions: Intracranial cavity volume is normally used to normalize brain tissue volumes to provide a tissue measure independent of head size. Therefore, the ability of the compared methods to provide an accurate ICV estimation has to be assessed. To this end, volume estimations using the different compared methods were obtained and compared to gold standard manual volumes. Figure 5 shows the automatic versus manual volume correlation for all the compared methods and dataset used. As can be noticed, the NICE method had highest overall correlation (0.976) while BEaST and VBM8 had 0.923 and 0.778, respectively. In Figure 6, a visual comparison of the segmentation results of three examples belonging to the three different subject populations can be performed.


Nonlocal intracranial cavity extraction.

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

Example segmentation results using NICE (a), BEaST (b), and VBM8 (c) methods on the three different population samples. Sagittal slices and 3D renderings of the segmentations are shown. Red voxels correspond to correct voxels in the segmentation compared to the gold standard. Blue voxels are false positives and green voxels are false negatives (AD case belongs to Oasis dataset and the adult and infant cases belong to the IXI dataset).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig6: Example segmentation results using NICE (a), BEaST (b), and VBM8 (c) methods on the three different population samples. Sagittal slices and 3D renderings of the segmentations are shown. Red voxels correspond to correct voxels in the segmentation compared to the gold standard. Blue voxels are false positives and green voxels are false negatives (AD case belongs to Oasis dataset and the adult and infant cases belong to the IXI dataset).
Mentions: Intracranial cavity volume is normally used to normalize brain tissue volumes to provide a tissue measure independent of head size. Therefore, the ability of the compared methods to provide an accurate ICV estimation has to be assessed. To this end, volume estimations using the different compared methods were obtained and compared to gold standard manual volumes. Figure 5 shows the automatic versus manual volume correlation for all the compared methods and dataset used. As can be noticed, the NICE method had highest overall correlation (0.976) while BEaST and VBM8 had 0.923 and 0.778, respectively. In Figure 6, a visual comparison of the segmentation results of three examples belonging to the three different subject populations can be performed.

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