Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks

Diagnostics

Ukihide Tateishi¹  Yuka Kikuchi¹  Yoshio Kitazume¹  Tomoyuki Fujioka¹  Kazunori Kubota¹  Mio Mori¹  Leona Katsuta¹  Tsuyoshi Nakagawa²  Mio Adachi²  Goshi Oda² 

[1] Department of Diagnostic Radiology, Tokyo Medical and Dental University Hospital 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan;Department of Surgery, Breast Surgery, Tokyo Medical and Dental University Hospital 1-5-45, Yushima, Bunkyo-ku, Tokyo 113-8501, Japan;

关键词: breast imaging;    ultrasound;    deep learning;    convolutional neural network;    generative adversarial networks;    artificial intelligence;   

DOI  :  10.3390/diagnostics9040176

来源: DOAJ

【 摘 要 】

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708−0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

【 授权许可】

Unknown