In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RC ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.
In population-oriented ergonomics product design and musculoskeletal kinetics analysis, digital spine models of different shape, pose and material property are in great demand. The purpose of this study was to construct a parameterized finite element spine model with adjustable spine shape and material property. We used statistical shape model approach to learn inter-subject shape variations from 65 CT images of training subjects. Second order polynomial regression was used to model the age-dependent changes in vertebral material property derived from spatially aligned CT images. Finally, a parametric spine generator was developed to create finite element instances of different shapes and material properties. For quantitative analysis, the generalization ability to emulate spine shapes of different people was evaluated by fitting into 17 test CT images. The median fitting accuracy was 0.8 for Dice coefficient and 0.43 mm for average surface distance. The age-dependent bone density regression curve was also proved to well agree with large population statistics data. Finite element simulation was performed to compare how shape parameters influenced the biomechanics distribution of spine. The proposed parametric finite element whole spine model will assist the design process of new devices and biomechanical simulation towards a wide range of population.
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