A Modified CycleGAN for Multi-Organ Ultrasound Image Enhancement via Unpaired Pre-Training

Abstract

Handheld ultrasound devices are known for their portability and affordability, making them widely utilized in underdeveloped areas and community healthcare for rapid diagnosis and early screening. However, the image quality of handheld ultrasound devices is not always satisfactory due to the limited equipment size, which hinders accurate diagnoses by doctors. At the same time, paired ultrasound images are difficult to obtain from the clinic because imaging process is complicated. Therefore, we propose a modified cycle generative adversarial network (cycleGAN) for ultrasound image enhancement from multiple organs via unpaired pre-training. We introduce an ultrasound image pre-training method that does not require paired images, alleviating the requirement for large-scale paired datasets. We also propose an enhanced block with different structures in the pre-training and fine-tuning phases, which can help achieve the goals of different training phases. To improve the robustness of the model, we add Gaussian noise to the training images as data augmentation. Our approach is effective in obtaining the best quantitative evaluation results using a small number of parameters and less training costs to improve the quality of handheld ultrasound devices.

Keywords

ultrasound image enhancement / handheld devices / unpaired images / pre-train and fine-tune / cycleGAN