Spatially regularized shape analysis of the hippocampus using P-spline based shape regression
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Spatially regularized shape analysis of the hippocampus using P-spline based shape regression. / Achterberg, Hakim Christiaan; de Rooi, Johan; Vernooij, Meike; Ikram, Arfan; Niessen, Wiro; Eilers, Paul; de Bruijne, Marleen.
In: IEEE Journal of Biomedical and Health Informatics, Vol. 24, No. 3, 2020, p. 825-834.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - Spatially regularized shape analysis of the hippocampus using P-spline based shape regression
AU - Achterberg, Hakim Christiaan
AU - de Rooi, Johan
AU - Vernooij, Meike
AU - Ikram, Arfan
AU - Niessen, Wiro
AU - Eilers, Paul
AU - de Bruijne, Marleen
PY - 2020
Y1 - 2020
N2 - Shape analysis is increasingly important to study changes in brain structures in relation to clinical neurological outcomes. This is a challenging task due to the high dimensionality of shape representations and the often limited number of available shapes. Current techniques counter the poor ratio between dimensions and sample size by using regularization in shape space, but do not take into account the spatial relations within the shapes. This can lead to models that are biologically implausible and difficult to interpret. We propose to use P-spline based regression, which combines a generalized linear model (GLM) with the coefficients described as B-splines and a penalty term that constrains the regression coefficients to be spatially smooth. Owing to the GLM, this method can naturally predict both continuous and discrete outcomes and can include non-spatial covariates without penalization. We evaluated our method on hippocampus shapes extracted from MR images of 510 non-demented, elderly people. We related the hippocampal shape to age, memory score, and sex. The proposed method retained the good performance of current techniques, such as ridge regression, but produced smoother coefficient fields that are easier to interpret.
AB - Shape analysis is increasingly important to study changes in brain structures in relation to clinical neurological outcomes. This is a challenging task due to the high dimensionality of shape representations and the often limited number of available shapes. Current techniques counter the poor ratio between dimensions and sample size by using regularization in shape space, but do not take into account the spatial relations within the shapes. This can lead to models that are biologically implausible and difficult to interpret. We propose to use P-spline based regression, which combines a generalized linear model (GLM) with the coefficients described as B-splines and a penalty term that constrains the regression coefficients to be spatially smooth. Owing to the GLM, this method can naturally predict both continuous and discrete outcomes and can include non-spatial covariates without penalization. We evaluated our method on hippocampus shapes extracted from MR images of 510 non-demented, elderly people. We related the hippocampal shape to age, memory score, and sex. The proposed method retained the good performance of current techniques, such as ridge regression, but produced smoother coefficient fields that are easier to interpret.
U2 - 10.1109/JBHI.2019.2926789
DO - 10.1109/JBHI.2019.2926789
M3 - Journal article
C2 - 31283491
VL - 24
SP - 825
EP - 834
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
SN - 2168-2194
IS - 3
ER -
ID: 227842907