Predicting Bone Degradation Using Vision Transformer and Synthetic Cellular Microstructures Dataset
This addresses the need for fast computational predictions of bone degradation in space exploration, though it is incremental as it builds on existing degradation models and synthetic data generation.
The study tackled the problem of predicting bone degradation for astronauts in microgravity by developing TransVNet, a deep-learning method that uses a hybrid 3D-CNN-VisionTransformer autoencoder to predict evolution from 3D voxelized images, trained on a synthetic dataset of bone-like microstructures.
Bone degradation, especially for astronauts in microgravity conditions, is crucial for space exploration missions since the lower applied external forces accelerate the diminution in bone stiffness and strength substantially. Although existing computational models help us understand this phenomenon and possibly restrict its effect in the future, they are time-consuming to simulate the changes in the bones, not just the bone microstructures, of each individual in detail. In this study, a robust yet fast computational method to predict and visualize bone degradation has been developed. Our deep-learning method, TransVNet, can take in different 3D voxelized images and predict their evolution throughout months utilizing a hybrid 3D-CNN-VisionTransformer autoencoder architecture. Because of limited available experimental data and challenges of obtaining new samples, a digital twin dataset of diverse and initial bone-like microstructures was generated to train our TransVNet on the evolution of the 3D images through a previously developed degradation model for microgravity.