399 research outputs found
MSKdeX: Musculoskeletal (MSK) decomposition from an X-ray image for fine-grained estimation of lean muscle mass and muscle volume
Musculoskeletal diseases such as sarcopenia and osteoporosis are major
obstacles to health during aging. Although dual-energy X-ray absorptiometry
(DXA) and computed tomography (CT) can be used to evaluate musculoskeletal
conditions, frequent monitoring is difficult due to the cost and accessibility
(as well as high radiation exposure in the case of CT). We propose a method
(named MSKdeX) to estimate fine-grained muscle properties from a plain X-ray
image, a low-cost, low-radiation, and highly accessible imaging modality,
through musculoskeletal decomposition leveraging fine-grained segmentation in
CT. We train a multi-channel quantitative image translation model to decompose
an X-ray image into projections of CT of individual muscles to infer the lean
muscle mass and muscle volume. We propose the object-wise intensity-sum loss, a
simple yet surprisingly effective metric invariant to muscle deformation and
projection direction, utilizing information in CT and X-ray images collected
from the same patient. While our method is basically an unpaired image-to-image
translation, we also exploit the nature of the bone's rigidity, which provides
the paired data through 2D-3D rigid registration, adding strong pixel-wise
supervision in unpaired training. Through the evaluation using a 539-patient
dataset, we showed that the proposed method significantly outperformed
conventional methods. The average Pearson correlation coefficient between the
predicted and CT-derived ground truth metrics was increased from 0.460 to
0.863. We believe our method opened up a new musculoskeletal diagnosis method
and has the potential to be extended to broader applications in multi-channel
quantitative image translation tasks. Our source code will be released soon.Comment: MICCAI 2023 early acceptance (12 pages and 6 figures
Large‑scale analysis of iliopsoas muscle volumes in the UK Biobank
Psoas muscle measurements are frequently used as markers of sarcopenia and predictors of health. Manually measured cross-sectional areas are most commonly used, but there is a lack of consistency regarding the position of the measurement and manual annotations are not practical for large population studies. We have developed a fully automated method to measure iliopsoas muscle volume (comprised of the psoas and iliacus muscles) using a convolutional neural network. Magnetic resonance images were obtained from the UK Biobank for 5000 participants, balanced for age, gender and BMI. Ninety manual annotations were available for model training and validation. The model showed excellent performance against out-of-sample data (average dice score coefficient of 0.9046 ± 0.0058 for six-fold cross-validation). Iliopsoas muscle volumes were successfully measured in all 5000 participants. Iliopsoas volume was greater in male compared with female subjects. There was a small but significant asymmetry between left and right iliopsoas muscle volumes. We also found that iliopsoas volume was significantly related to height, BMI and age, and that there was an acceleration in muscle volume decrease in men with age. Our method provides a robust technique for measuring iliopsoas muscle volume that can be applied to large cohorts
Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer
Data-driven methods have become increasingly more prominent for
musculoskeletal modelling due to their conceptually intuitive simple and fast
implementation. However, the performance of a pre-trained data-driven model
using the data from specific subject(s) may be seriously degraded when
validated using the data from a new subject, hindering the utility of the
personalised musculoskeletal model in clinical applications. This paper
develops an active physics-informed deep transfer learning framework to enhance
the dynamic tracking capability of the musculoskeletal model on the unseen
data. The salient advantages of the proposed framework are twofold: 1) For the
generic model, physics-based domain knowledge is embedded into the loss
function of the data-driven model as soft constraints to penalise/regularise
the data-driven model. 2) For the personalised model, the parameters relating
to the feature extraction will be directly inherited from the generic model,
and only the parameters relating to the subject-specific inference will be
finetuned by jointly minimising the conventional data prediction loss and the
modified physics-based loss. In this paper, we use the synchronous muscle
forces and joint kinematics prediction from surface electromyogram (sEMG) as
the exemplar to illustrate the proposed framework. Moreover, convolutional
neural network (CNN) is employed as the deep neural network to implement the
proposed framework, and the physics law between muscle forces and joint
kinematics is utilised as the soft constraints. Results of comprehensive
experiments on a self-collected dataset from eight healthy subjects indicate
the effectiveness and great generalization of the proposed framework.Comment: arXiv admin note: text overlap with arXiv:2207.0143
Overview and Evaluation of a Computational Bone Physiology Modeling Toolchain and Its Application to Testing of Exercise Countermeasures
Prolonged microgravity exposure disrupts natural bone remodeling processes and can lead to a significant loss of bone strength, increasing injury risk during missions and placing astronauts at a greater risk of bone fracture later in life. Resistance-based exercise during missions is used to combat bone loss, but current exercise countermeasures do not completely mitigate the effects of microgravity. To address this concern, we present work to develop a personalizable, site-specific computational modeling toolchain of bone remodeling dynamics to understand and estimate changes in volumetric bone mineral density (BMD) in response to microgravity-induced bone unloading and in-flight exercise. The toolchain is evaluated against data collected from subjects in a 70-day bedrest study and is found to provide insight into the amount of exercise stimulus needed to minimize bone loss, quantitatively predicting post-study volumetric BMD of control subjects who did not perform exercise, and qualitatively predicting the effects of exercise. Results suggest that, with additional data, the toolchain could be improved to aid in developing customized in-flight exercise regimens and predict exercise effectiveness
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