Abstract

This paper investigates the use of the Bernstein Basis Function network for reconstructing accurate geometries of bones from medical images. Accurate models of bone geometry are indispensable for biomedical applications, especially in designing customized orthopedic implants. The two-layer neural architecture BBF network uses nonlinear Bernstein polynomials to perform curve and surface fitting, where the generated weights during training act as control points for Bézier curves. The BBF network adjusts the number of basis neurons so that curve fitting accuracy is optimally balanced with smoothness, addressing weaknesses inherent in traditional and earlier neural network methods. The constraints of positional and tangential continuity are incorporated into the learning algorithm to improve geometric consistency. Quantitative analysis has shown that the BBF network significantly improves the precision of curve fitting, reduces the roughness of reconstructions, and outperforms other methods in simulation studies. Experiments in vivo further validate its clinical usability, showing its ability to reproduce complex geometries with high accuracy in bone reproductions. This study also shows that the BBF network can be a crucial innovation in medical imaging where anatomical modeling and personalized medicine can be accomplished robustly. Some limitations include: dependency on certain imaging techniques and dataset biases. As such, the future course of work involves broader validations across various imaging techniques.