Barkat Lilach, Freiman Moti, Azhari Haim
Biomedical Engineering Faculty, Technion-Israel Institute of Technology, Haifa 3200001, Israel.
Bioengineering (Basel). 2023 Mar 22;10(3):388. doi: 10.3390/bioengineering10030388.
Ultrasound imaging is cost effective, radiation-free, portable, and implemented routinely in clinical procedures. Nonetheless, image quality is characterized by a granulated appearance, a poor SNR, and speckle noise. Specific for breast tumors, the margins are commonly blurred and indistinct. Thus, there is a need for improving ultrasound image quality. We hypothesize that this can be achieved by translation into a more realistic display which mimics a pseudo anatomical cut through the tissue, using a cycle generative adversarial network (CycleGAN). In order to train CycleGAN for this translation, two datasets were used, "Breast Ultrasound Images" (BUSI) and a set of optical images of poultry breast tissues. The generated pseudo anatomical images provide improved visual discrimination of the lesions through clearer border definition and pronounced contrast. In order to evaluate the preservation of the anatomical features, the lesions in both datasets were segmented and compared. This comparison yielded median dice scores of 0.91 and 0.70; median center errors of 0.58% and 3.27%; and median area errors of 0.40% and 4.34% for the benign and malignancies, respectively. In conclusion, generated pseudo anatomical images provide a more intuitive display, enhance tissue anatomy, and preserve tumor geometry; and can potentially improve diagnoses and clinical outcomes.
超声成像具有成本效益、无辐射、便携且在临床程序中常规使用。尽管如此，其图像质量的特点是呈现颗粒状外观、信噪比低和散斑噪声。对于乳腺肿瘤而言，其边界通常模糊不清。因此，需要提高超声图像质量。我们假设通过使用循环生成对抗网络（CycleGAN）将其转换为更逼真的显示，模拟穿过组织的伪解剖切面，可以实现这一目标。为了训练CycleGAN进行这种转换，使用了两个数据集，即“乳腺超声图像”（BUSI）和一组家禽乳腺组织的光学图像。生成的伪解剖图像通过更清晰的边界定义和明显的对比度，提高了对病变的视觉辨别能力。为了评估解剖特征的保留情况，对两个数据集中的病变进行了分割和比较。对于良性和恶性病变，这种比较分别得出的骰子系数中位数为0.91和0.70；中心误差中位数为0.58%和3.27%；面积误差中位数为0.40%和4.34%。总之，生成的伪解剖图像提供了更直观的显示，增强了组织解剖结构，并保留了肿瘤的几何形状；并有可能改善诊断和临床结果。
