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

• URL: http://arxiv.org/abs/2208.03934v3
• Date: Tue, 5 Dec 2023 23:59:59 GMT
• Title: Inflating 2D Convolution Weights for Efficient Generation of 3D Medical Images
• Authors: Yanbin Liu, Girish Dwivedi, Farid Boussaid, Frank Sanfilippo, Makoto Yamada, and Mohammed Bennamoun
• Abstract summary: Two problems prevent effective training of a 3D medical generative model: 3D medical images are expensive to acquire and annotate, and a large number of parameters are involved in 3D convolution. We propose a novel GAN model called 3D Split&Shuffle-GAN. We show that our method leads to improved 3D image generation quality with significantly fewer parameters.
• Score: 35.849240945334
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The generation of three-dimensional (3D) medical images has great application potential since it takes into account the 3D anatomical structure. Two problems prevent effective training of a 3D medical generative model: (1) 3D medical images are expensive to acquire and annotate, resulting in an insufficient number of training images, and (2) a large number of parameters are involved in 3D convolution. Methods: We propose a novel GAN model called 3D Split&Shuffle-GAN. To address the 3D data scarcity issue, we first pre-train a two-dimensional (2D) GAN model using abundant image slices and inflate the 2D convolution weights to improve the initialization of the 3D GAN. Novel 3D network architectures are proposed for both the generator and discriminator of the GAN model to significantly reduce the number of parameters while maintaining the quality of image generation. Several weight inflation strategies and parameter-efficient 3D architectures are investigated. Results: Experiments on both heart (Stanford AIMI Coronary Calcium) and brain (Alzheimer's Disease Neuroimaging Initiative) datasets show that our method leads to improved 3D image generation quality (14.7 improvements on Fr\'echet inception distance) with significantly fewer parameters (only 48.5% of the baseline method). Conclusions: We built a parameter-efficient 3D medical image generation model. Due to the efficiency and effectiveness, it has the potential to generate high-quality 3D brain and heart images for real use cases.

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