Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network
This work addresses the challenge of domain distribution discrepancies in carotid ultrasound images arising from different imaging devices. To mitigate this issue, the authors propose a novel generative adversarial network (GAN) architecture that formulates domain adaptation as an image-to-image translation task. The method simultaneously achieves texture transfer and reverberation noise suppression while preserving anatomical structures. Evaluated on two three-domain carotid ultrasound datasets, the approach substantially outperforms existing techniques such as CycleGAN, significantly enhancing cross-domain consistency. Quantitative results demonstrate high histogram correlation coefficients of 0.960 and 0.920, along with reduced Bhattacharyya distances of 0.040 and 0.085, thereby eliminating performance degradation on new devices and avoiding the need for costly retraining.