AFSegNet: few-shot 3D ankle-foot bone segmentation via hierarchical feature distillation and multi-scale attention and fusion.

Accurate segmentation of ankle and foot bones from CT scans is essential for morphological analysis. Ankle and foot bone segmentation challenges due to the blurred bone boundaries, narrow inter-bone gaps, gaps in the cortical shell, and uneven spongy bone textures. Our study endeavors to create a deep learning framework that harnesses advantages of 3D deep learning and tackles the hurdles in accurately segmenting ankle and foot bones from clinical CT scans.

Material Properties of Fiber Bundles of the Superficial Medial Collateral Ligament of the Knee Joint.

The superficial medial collateral ligament (sMCL) of the human knee joint has functionally separate anterior and posterior fiber bundles. The two bundles are alternatively loaded as the knee flexion angle changes during walking. To date, the two bundles are usually not distinguished in knee ligament simulations because there has been little information about their material properties.

Can we reposition finite element human body model like dummies?

Rapidly repositioning finite element human body models (FE-HBMs) with high biofidelity is an important but notorious problem in vehicle safety and injury biomechanics. We propose to reposition the FE-HBM in a dummy-like manner, i.e.

Influence of walking on knee ligament response in car-to-pedestrian collisions.

Pedestrians are likely to experience walking before accidents. The walking process imposes cyclic loading on knee ligaments and increases knee joint temperature. Both cyclic loading and temperature affect the material properties of ligaments, which further influence the risk of ligament injury.

Self-Supervised Learning for Non-Rigid Registration Between Near-Isometric 3D Surfaces in Medical Imaging.

Non-rigid registration between 3D surfaces is an important but notorious problem in medical imaging, because finding correspondences between non-isometric instances is mathematically non-trivial. We propose a novel self-supervised method to learn shape correspondences directly from a group of bone surfaces segmented from CT scans, without any supervision from time-consuming and error-prone manual annotations. Relying on a Siamese architecture, DiffusionNet as the feature extractor is jointly trained with a pair of randomly rotated and scaled copies of the same shape.

Assessment of brain injury characterization and influence of modeling approaches.

In this study, using computational biomechanics models, we investigated influence of the skull-brain interface modeling approach and the material property of cerebrum on the kinetic, kinematic and injury outputs. Live animal head impact tests of different severities were reconstructed in finite element simulations and DAI and ASDH injury results were compared. We used the head/brain models of Total HUman Model for Safety (THUMS) and Global Human Body Models Consortium (GHBMC), which had been validated under several loading conditions.

A GAN based approach for inferring progression trajectories of costal cartilage calcification from cross-sectional data at image level.

Background: Costal cartilage calcification (CCC) increases with age and presents differently for men and women. In individuals, however, the cross-sectional studies that show such trends do not reveal the geometric trajectories through which calcification might accumulate across a lifetime. Generative adversarial networks have the potential to reveal such trajectories from cross-sectional data by learning population trends and synthesizing individualized images at progressive levels of calcification.

Obesity effects on pedestrian lower extremity injuries in vehicle-to-pedestrian impacts: A numerical investigation using human body models.

Objective: The objectives of this study were to develop a method for modeling obese pedestrians and to investigate effects of obesity on pedestrian impact responses and injury outcomes.

Methods: The GHBMC (Global Human Body Model Consortium) 50th percentile male pedestrian model was morphed into geometries with 4 body mass index (BMI) levels (25/30/35/40 kg/m) predicted by statistical body shape models. Each of the 4 morphed models was further morphed from a standing posture into 2 other gaits (toe-off and mid-swing), which resulted in a total of 12 (4 BMIs × 3 postures) models.