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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.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.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 of mask differences between our mask definition (b) and BEaST mask (c) for an Alzheimer's case (a). As can be noticed, all external CSF is included in NICE mask while this is not case at the corresponding BEaST mask (example case from Oasis dataset).
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fig2: Example of mask differences between our mask definition (b) and BEaST mask (c) for an Alzheimer's case (a). As can be noticed, all external CSF is included in NICE mask while this is not case at the corresponding BEaST mask (example case from Oasis dataset).

Mentions: To generate the template masks we followed a similar approach as described in BEaST paper since full manual labeling was too time consuming and error prone as discussed in Eskildsen et al. [11]. All template images in the library were automatically segmented using BEaST software to have an initial mask. Conditional mask dilation (only over CSF voxels) was applied to include external CSF not already included in the BEaST mask. Finally, all the images were manually corrected by an expert on brain anatomy using the ITK-SNAP software [31] to remove segmentation errors. In Figure 2 we show an example of our mask definition compared to BEaST definition for a patient with Alzheimer's disease.


Nonlocal intracranial cavity extraction.

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

Example of mask differences between our mask definition (b) and BEaST mask (c) for an Alzheimer's case (a). As can be noticed, all external CSF is included in NICE mask while this is not case at the corresponding BEaST mask (example case from Oasis dataset).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

fig2: Example of mask differences between our mask definition (b) and BEaST mask (c) for an Alzheimer's case (a). As can be noticed, all external CSF is included in NICE mask while this is not case at the corresponding BEaST mask (example case from Oasis dataset).
Mentions: To generate the template masks we followed a similar approach as described in BEaST paper since full manual labeling was too time consuming and error prone as discussed in Eskildsen et al. [11]. All template images in the library were automatically segmented using BEaST software to have an initial mask. Conditional mask dilation (only over CSF voxels) was applied to include external CSF not already included in the BEaST mask. Finally, all the images were manually corrected by an expert on brain anatomy using the ITK-SNAP software [31] to remove segmentation errors. In Figure 2 we show an example of our mask definition compared to BEaST definition for a patient with Alzheimer's disease.

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.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.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