Thomas Küstner1,2,3, Tobias Hepp2, Karim Armanious2,3, Konstantin Nikolaou4, Sergios Gatidis2,4, and Bin Yang3
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Medical Image and Data Analysis (MIDAS), University Hospital Tübingen, Tübingen, Germany, 3Institute of Signal Processing and System Theory, University of Stuttgart, Stuttgart, Germany, 4Department of Radiology, University Hospital Tübingen, Tübingen, Germany
Synopsis
Age
is one of the most important clinical parameters describing patients in a medical
context. The chronological age (CA) does however not necessarily reflect the
true underlying biological age (BA) which can depend on multiple factors such
as lifestyle, social environment, medical history, genetics and ethnicity. It
is therefore desirable to measure BA quantitatively and objectively. In this
proof-of-principle study, we examine if CA can be estimated from whole-body
MRI. We propose a novel deep learning architecture to perform an accurate CA
estimation.
Introduction
Each human ages differently depending on
multiple factors such as lifestyle, social environment, medical history,
genetics and ethnicity. Age is one of the most important parameters describing
individuals in a medical context. In many clinical conditions, characteristic
age distributions define a-priori probabilities for initial staging, therapy selection,
drug administration and treatment monitoring1-3.
However, the chronological age (CA) does not
necessarily coincide with the actual biological age (BA) of the human body or
of one specific organ. In fact, due to these age-related effects the general
condition and specific capacities of individuals can deviate significantly from
the average of the respective age group. These observations have motivated the
concept of BA in contrast to CA4 and hence BA can be viewed as a
measure of deviation from the age group. Despite of this vague definition of
BA, the potential benefits for a personalized treatment are easily conceivable.
However, the question remains on how to measure BA quantitatively and
objectively.
Different age measures have been proposed in
the past based on genetic, cellular, phenotypic and epidemiologic approaches4-10. In addition, clinical parameters such
as functional parameters (e.g. lung function, cardiac function, blood pressure)
or anthropometric measures (e.g. gender, body mass index, fat distribution)
have been included8,10 in these studies. In principle, the
use of medical imaging can be expected to provide substantial information. The assessment
of age with MRI was mainly limited to skeletal9,11 and neurological10,12-14 applications. Manually extracted
morphological features were used15 with machine learning
classifications16,17. More recently, convolutional
neural networks (CNNs) have been applied to predict the brain age from 2D MR
images18,19 or from the whole volume with
shallow 3D CNNs14,20.
None of the previous works focused on age estimation
in a whole-body setting. Before an organ-specific BA can be estimated from
whole-body MR images, it is required to accurately estimate the CA. In this
work, we investigate the hypothesis that: CA is highly correlated with the
image content. Moreover to be unbiased by any organ-specific features, the age
estimation is done blind, i.e. on the whole-body T1w image without organ
information. A CNN is proposed which performs a regression based on multiple 2D
image orientations from the whole-body 3D MR and on anthropometric features. Influence
of training database size and anthropometric features is investigated and the
proposed network is compared against a CNN used for brain age estimation (deep
Gaussian processes; DPG)13.Methods
A hybrid 3D CNN is proposed for CA estimation
which uses as input three orthogonal 2D images in axial, coronal and sagittal
view of a multi breath-hold 3D T1w DIXON sequence (native axial, TE=1.23/2.46ms,
TR=4.36ms, resolution=1.4x1.4x3mm, matrix size=320x260x316). Data was acquired
in a multi-center epidemiological cohort study (German National Cohort; NAKO)21 on a 3T MRI. 200 subjects were
randomly selected to uniformly represent age and gender as depicted in Fig.1.
The training database consists of 157 subjects and 43 subjects are used for
testing. Central slices of each orientation are normalized and taken as input
(Fig.2).
A blind CA estimation is investigated, i.e. no
explicit attention focusing or pre-segmented organs are used, in order to test
the hypothesis that CA is correlated to the image content, which is the
pre-cursor for BA determination. Similar to previous works, we incorporate epidemiological
parameters such as gender, height and weight of the subject into the network
prediction.
The proposed AgeNet is depicted in Fig.3 and is
composed of three parallel branches with a 2D CNN feature extractor and a
squeeze-and-excitation22 for attention focusing. The outputs
are then combined by a global average pooling and concatenated together with
the epidemiological parameters to be further processed in three fully-connected
layers with CA regression output. The network is trained end-to-end with Adam
optimizer and mean absolute error (MAE) loss for 100 epochs and learning rate
10-4.
Difference between CA and predicted CA (PCA)
are compared for various training scenarios (see Fig.4) and against a network
for brain age estimation (DGP)13. Statistical significance was
evaluated with a paired Welch’s t-test ($$$P<0.05$$$)
and
Bonferroni correction.Results and Discussion
Fig.4 shows the comparison between the proposed
AgeNet with and without epidemiological parameters, trained only on male/female
subjects and using a smaller training database of 75%, 50% and 25%. AgeNet
performs statistically significant better than all other approaches (except
female only) (Fig.4a) and providing smallest MAE=3.2±2.3years (Fig.4b), minimal
bias=-0.5years and tightest confidence intervals=±7.8years (Fig.4c). Without epidemiological
parameters, the estimation is still feasible, but with larger uncertainty. Thus,
epidemiological parameters help to focus the network’s attention. The DPG
network fails to predict a reliable CA. Inspecting the Grad-CAM23 backpropagations in Fig.5, the
network focused its decision making mainly on larger organs (liver) and the
spine. This motivates thus the extension in the future to concentrate on an
organ-orientated CA estimation from which subsequently a BA can be derived.
This study has limitations. No organ-specific
estimation was conducted. Due to limited training database, only a multi-slice
2D processing was feasible. Moreover, some age groups are currently
underrepresented in this database.Conclusion
CA
estimation from whole-body MRI is feasible with a reliable estimation of
MAE=3.2 years.Acknowledgements
No acknowledgement found.References
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