3D Reconstruction of Vertebrae from Sparse 2D CT Data Using Conditional GANs and Transfer Learning

A three dimensional reconstruction of vertebrae from CT Scans is necessary for surgical planning, medical teaching and diagnosis. Traditional methods are computationally expensive, while state-of-the art deep learning techniques lack in effective handling of sparse or incomplete data.We propose a deep learning pipeline composed of transfer learning, convolutional autoencoders, and cGANs for reconstructing 3D point clouds of vertebrae from sparse 2D CT slices.The feature extractor, based on Mobil