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The generation of three-dimensional (3D) medical images has great applicationpotential since it takes into account the 3D anatomical structure. Two problemsprevent effective training of a 3D medical generative model: (1) 3D medicalimages are expensive to acquire and annotate, resulting in an insufficientnumber of training images, and (2) a large number of parameters are involved in3D convolution.  Methods: We propose a novel GAN model called 3D Split&Shuffle-GAN. To addressthe 3D data scarcity issue, we first pre-train a two-dimensional (2D) GAN modelusing abundant image slices and inflate the 2D convolution weights to improvethe initialization of the 3D GAN. Novel 3D network architectures are proposedfor both the generator and discriminator of the GAN model to significantlyreduce the number of parameters while maintaining the quality of imagegeneration. Several weight inflation strategies and parameter-efficient 3Darchitectures are investigated.  Results: Experiments on both heart (Stanford AIMI Coronary Calcium) and brain(Alzheimer's Disease Neuroimaging Initiative) datasets show that our methodleads to improved 3D image generation quality (14.7 improvements on Fr\'echetinception distance) with significantly fewer parameters (only 48.5% of thebaseline method).  Conclusions: We built a parameter-efficient 3D medical image generationmodel. Due to the efficiency and effectiveness, it has the potential togenerate high-quality 3D brain and heart images for real use cases.

Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images