Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network

• URL: http://arxiv.org/abs/2601.01460v1
• Date: Sun, 04 Jan 2026 10:08:36 GMT
• Title: Domain Adaptation of Carotid Ultrasound Images using Generative Adversarial Network
• Authors: Mohd Usama, Belal Ahmad, Christer Gronlund, Faleh Menawer R Althiyabi,
• Abstract summary: We propose a novel Generative Adversarial Network (GAN)-based model for image-to-image translation.<n>We show that the model successfully translated the texture pattern of images and removed reverberation noise from the ultrasound images.
• Score: 0.2624902795082451
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning has been extensively used in medical imaging applications, assuming that the test and training datasets belong to the same probability distribution. However, a common challenge arises when working with medical images generated by different systems or even the same system with different parameter settings. Such images contain diverse textures and reverberation noise that violate the aforementioned assumption. Consequently, models trained on data from one device or setting often struggle to perform effectively with data from other devices or settings. In addition, retraining models for each specific device or setting is labor-intensive and costly. To address these issues in ultrasound images, we propose a novel Generative Adversarial Network (GAN)-based model. We formulated the domain adaptation tasks as an image-to-image translation task, in which we modified the texture patterns and removed reverberation noise in the test data images from the source domain to align with those in the target domain images while keeping the image content unchanged. We applied the proposed method to two datasets containing carotid ultrasound images from three different domains. The experimental results demonstrate that the model successfully translated the texture pattern of images and removed reverberation noise from the ultrasound images. Furthermore, we evaluated the CycleGAN approaches for a comparative study with the proposed model. The experimental findings conclusively demonstrated that the proposed model achieved domain adaptation (histogram correlation (0.960 (0.019), & 0.920 (0.043) and bhattacharya distance (0.040 (0.020), & 0.085 (0.048)), compared to no adaptation (0.916 (0.062) & 0.890 (0.077), 0.090 (0.070) & 0.121 (0.095)) for both datasets.

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