Ultrasound is the second most used modality in medical imaging. It is cost
effective, hazardless, portable and implemented routinely in numerous clinical
procedures. Nonetheless, image quality is characterized by granulated
appearance, poor SNR and speckle noise. Specific for malignant tumors, the
margins are blurred and indistinct. Thus, there is a great need for improving
ultrasound image quality. We hypothesize that this can be achieved, using
neural networks, by translation into a more realistic display which mimics an
anatomical cut through the tissue. In order to achieve this goal, the
preferable approach would be to use a set of paired images. However, this is
practically impossible in our case. Therefore, Cycle Generative Adversarial
Network (CycleGAN) was used, in order to learn each domain properties
separately and enforce cross domain cycle consistency. The two datasets which
were used for training the model were "Breast Ultrasound Images" (BUSI) and a
set of optic images of poultry breast tissue samples acquired at our lab. The
generated pseudo anatomical images provide improved visual discrimination of
the lesions with clearer border definition and pronounced contrast. In order to
evaluate the preservation of the anatomical features, the lesions in the
ultrasonic images and the generated pseudo anatomical images were both
automatically segmented and compared. This comparison yielded median dice score
of 0.91 for the benign tumors and 0.70 for the malignant ones. The median
lesion center error was 0.58% and 3.27% for the benign and malignancies
respectively and the median area error index was 0.40% and 4.34% for the benign
and malignancies respectively. In conclusion, these generated pseudo anatomical
images, which are presented in a more intuitive way, enhance tissue anatomy and
can potentially simplify the diagnosis and improve the clinical outcome.

通过利用神经网络的 CycleGAN 方法，从医学超声图像数据中生成伪解剖图像，提高图像质量以更好地表现病变区域，其中生成伪解剖图像在病变分割中的对比度和区分度较高，可潜在地简化诊断流程并改善临床结果。