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Measuring brain atrophy with a generalized formulation of the boundary shift integral.

Prados F, Cardoso MJ, Leung KK, Cash DM, Modat M, Fox NC, Wheeler-Kingshott CA, Ourselin S, Alzheimer's Disease Neuroimaging Initiati - Neurobiol. Aging (2014)

Bottom Line: Brain atrophy measured using structural magnetic resonance imaging (MRI) has been widely used as an imaging biomarker for disease diagnosis and tracking of pathologic progression in neurodegenerative diseases.In this work, we present a generalized and extended formulation of the boundary shift integral (gBSI) using probabilistic segmentations to estimate anatomic changes between 2 time points.This method adaptively estimates a non-binary exclusive OR region of interest from probabilistic brain segmentations of the baseline and repeat scans to better localize and capture the brain atrophy.

View Article: PubMed Central - PubMed

Affiliation: Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK. Electronic address: f.carrasco@ucl.ac.uk.

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Comparison between binary XOR of the previous BSI (Freeborough and Fox, 1997; Leung et al., 2010b, 2012), fuzzy XOR of pBSI with γ ≡ 1 and γ ≡ 0.5 (Ledig et al., 2012) and probabilistic weighted XOR of gBSI. X axis represent the tissue displacement along the boundary, Y axis represent segmentation probabilities and red lines represent the probabilistic segmentation of the baseline and repeat images. Different boundary shifts and slope-rates are used to simulate a control (A) and an AD brain (B). The yellow line is the representation of the binary XOR from the classic BSI. This region of interest is produced by thresholding the probability at 0.5 followed by the dilation and/or erosion of the boundaries for XOR estimation. The magenta line is fuzzy XOR of pBSI with γ ≡ 1, and the partial overlapped dashed green line is using γ ≡ 0.5. Dashed blue line is the estimated pXOR, and blue line corresponds to RpxOR. Abbreviations: AD, Alzheimer's disease; BSI, boundary shift integral; gBSI, generalized boundary shift integral. (For interpretation of the references to color in this Figure, the reader is referred to the web version of this article.)
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fig3: Comparison between binary XOR of the previous BSI (Freeborough and Fox, 1997; Leung et al., 2010b, 2012), fuzzy XOR of pBSI with γ ≡ 1 and γ ≡ 0.5 (Ledig et al., 2012) and probabilistic weighted XOR of gBSI. X axis represent the tissue displacement along the boundary, Y axis represent segmentation probabilities and red lines represent the probabilistic segmentation of the baseline and repeat images. Different boundary shifts and slope-rates are used to simulate a control (A) and an AD brain (B). The yellow line is the representation of the binary XOR from the classic BSI. This region of interest is produced by thresholding the probability at 0.5 followed by the dilation and/or erosion of the boundaries for XOR estimation. The magenta line is fuzzy XOR of pBSI with γ ≡ 1, and the partial overlapped dashed green line is using γ ≡ 0.5. Dashed blue line is the estimated pXOR, and blue line corresponds to RpxOR. Abbreviations: AD, Alzheimer's disease; BSI, boundary shift integral; gBSI, generalized boundary shift integral. (For interpretation of the references to color in this Figure, the reader is referred to the web version of this article.)

Mentions: The differences between the binary XOR and pXOR are shown in Fig. 3. Fig. 3A shows low uncertainty and small shift between 2 masks, reflecting the situation where the boundaries are well defined, that is, for control patients. On the other hand, Fig. 3B represents a high uncertainty configuration, simulating the existence of atrophy and uncertainty between the 2 time points, that is, in AD patients.


Measuring brain atrophy with a generalized formulation of the boundary shift integral.

Prados F, Cardoso MJ, Leung KK, Cash DM, Modat M, Fox NC, Wheeler-Kingshott CA, Ourselin S, Alzheimer's Disease Neuroimaging Initiati - Neurobiol. Aging (2014)

Comparison between binary XOR of the previous BSI (Freeborough and Fox, 1997; Leung et al., 2010b, 2012), fuzzy XOR of pBSI with γ ≡ 1 and γ ≡ 0.5 (Ledig et al., 2012) and probabilistic weighted XOR of gBSI. X axis represent the tissue displacement along the boundary, Y axis represent segmentation probabilities and red lines represent the probabilistic segmentation of the baseline and repeat images. Different boundary shifts and slope-rates are used to simulate a control (A) and an AD brain (B). The yellow line is the representation of the binary XOR from the classic BSI. This region of interest is produced by thresholding the probability at 0.5 followed by the dilation and/or erosion of the boundaries for XOR estimation. The magenta line is fuzzy XOR of pBSI with γ ≡ 1, and the partial overlapped dashed green line is using γ ≡ 0.5. Dashed blue line is the estimated pXOR, and blue line corresponds to RpxOR. Abbreviations: AD, Alzheimer's disease; BSI, boundary shift integral; gBSI, generalized boundary shift integral. (For interpretation of the references to color in this Figure, the reader is referred to the web version of this article.)
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4288791&req=5

fig3: Comparison between binary XOR of the previous BSI (Freeborough and Fox, 1997; Leung et al., 2010b, 2012), fuzzy XOR of pBSI with γ ≡ 1 and γ ≡ 0.5 (Ledig et al., 2012) and probabilistic weighted XOR of gBSI. X axis represent the tissue displacement along the boundary, Y axis represent segmentation probabilities and red lines represent the probabilistic segmentation of the baseline and repeat images. Different boundary shifts and slope-rates are used to simulate a control (A) and an AD brain (B). The yellow line is the representation of the binary XOR from the classic BSI. This region of interest is produced by thresholding the probability at 0.5 followed by the dilation and/or erosion of the boundaries for XOR estimation. The magenta line is fuzzy XOR of pBSI with γ ≡ 1, and the partial overlapped dashed green line is using γ ≡ 0.5. Dashed blue line is the estimated pXOR, and blue line corresponds to RpxOR. Abbreviations: AD, Alzheimer's disease; BSI, boundary shift integral; gBSI, generalized boundary shift integral. (For interpretation of the references to color in this Figure, the reader is referred to the web version of this article.)
Mentions: The differences between the binary XOR and pXOR are shown in Fig. 3. Fig. 3A shows low uncertainty and small shift between 2 masks, reflecting the situation where the boundaries are well defined, that is, for control patients. On the other hand, Fig. 3B represents a high uncertainty configuration, simulating the existence of atrophy and uncertainty between the 2 time points, that is, in AD patients.

Bottom Line: Brain atrophy measured using structural magnetic resonance imaging (MRI) has been widely used as an imaging biomarker for disease diagnosis and tracking of pathologic progression in neurodegenerative diseases.In this work, we present a generalized and extended formulation of the boundary shift integral (gBSI) using probabilistic segmentations to estimate anatomic changes between 2 time points.This method adaptively estimates a non-binary exclusive OR region of interest from probabilistic brain segmentations of the baseline and repeat scans to better localize and capture the brain atrophy.

View Article: PubMed Central - PubMed

Affiliation: Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, UK. Electronic address: f.carrasco@ucl.ac.uk.

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Related in: MedlinePlus