Missing Data in Longitudinal Image Retrieval for Alzheimer’s Disease
Date Issued
2022
Author(s)
Trojachanec Dineva, Katarina
Abstract
The paper is focused on the missing scans in the
context of longitudinal image retrieval for Alzheimer's Disease.
Namely, we explore the influence of missing data on the retrieval
results when the subjects are represented by the longitudinal
changes calculated on the basis of the within-subject template
generated using the available time points. To evaluate the effect
of the missing scans, we defined two (most characteristic and
most common) scenarios, in which missing scans at a specific
time point are considered, and one scenario that is based on
complete data used as a baseline to compare against.
Additionally, we increased the number of patients with missing
scans from 10% to 50% and evaluated its impact on the retrieval
results.
The evaluation showed that from the examined types of
feature vectors, concatenated longitudinal changes of the
volumes of the cortical and sub-cortical structures are superior
and robust. In the case when the dimensionality of the descriptor
is an important criterion, we recommend the usage of the
percent change or symmetrized percent change of the
volumetric measures. Additionally, the influence of the missing
scans on the retrieval results is lower when incomplete data
occurs in the early time points, rather than in later ones.
Moreover, very little or no performance reduction was detected
by increasing the number of subjects with missing scans. In
general, the evaluation showed very small or no performance
degradation in the retrieval process in the scenarios with
missing scans, in comparison to the scenario with fully complete
data.
context of longitudinal image retrieval for Alzheimer's Disease.
Namely, we explore the influence of missing data on the retrieval
results when the subjects are represented by the longitudinal
changes calculated on the basis of the within-subject template
generated using the available time points. To evaluate the effect
of the missing scans, we defined two (most characteristic and
most common) scenarios, in which missing scans at a specific
time point are considered, and one scenario that is based on
complete data used as a baseline to compare against.
Additionally, we increased the number of patients with missing
scans from 10% to 50% and evaluated its impact on the retrieval
results.
The evaluation showed that from the examined types of
feature vectors, concatenated longitudinal changes of the
volumes of the cortical and sub-cortical structures are superior
and robust. In the case when the dimensionality of the descriptor
is an important criterion, we recommend the usage of the
percent change or symmetrized percent change of the
volumetric measures. Additionally, the influence of the missing
scans on the retrieval results is lower when incomplete data
occurs in the early time points, rather than in later ones.
Moreover, very little or no performance reduction was detected
by increasing the number of subjects with missing scans. In
general, the evaluation showed very small or no performance
degradation in the retrieval process in the scenarios with
missing scans, in comparison to the scenario with fully complete
data.
Subjects
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