A Pseudo-Siamese Feature Fusion Generative Adversarial Network for Synthesizing High-Quality Fetal Four-Chamber Views

被引：13

作者：

Qiao, Sibo ^{[1
]}

Pan, Silin ^{[2
]}

Luo, Gang ^{[2
]}

Pang, Shanchen ^{[1
]}

Chen, Taotao ^{[3
]}

Singh, Amit Kumar ^{[4
]}

Lv, Zhihan ^{[5
]}

机构：

[1] Univ Petr, Sch Comp Sci & Technol, Qingdao 266580, Shandong, Peoples R China

[2] Women & Childrens Hosp, Heart Ctr, Qingdao 266034, Shandong, Peoples R China

[3] Women & Childrens Hosp, Dept Ultrasound, Qingdao 266034, Shandong, Peoples R China

[4] Natl Inst Technol, Dept Comp Sci & Engn, Patna 800005, India

[5] Uppsala Univ, Fac Arts, Dept Game Design, S-75105 Uppsala, Sweden

来源：

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS | 2023年 / 27卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Generative adversarial networks; Generators; Deep learning; Image synthesis; Training; Speckle; Feature extraction; Congenital heart disease; deep learning; GAN; images synthesis; fetal four-chamber sketch images; fetal four-chamber views; CONVOLUTIONAL NEURAL-NETWORKS; SEGMENTATION;

D O I：

10.1109/JBHI.2022.3143319

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Four-chamber (FC) views are the primary ultrasound(US) images that cardiologists diagnose whether the fetus has congenital heart disease (CHD) in prenatal diagnosis and screening. FC views intuitively depict the developmental morphology of the fetal heart. Early diagnosis of fetal CHD has always been the focus and difficulty of prenatal screening. Furthermore, deep learning technology has achieved great success in medical image analysis. Hence, applying deep learning technology in the early screening of fetal CHD helps improve diagnostic accuracy. However, the lack of large-scale and high-quality fetal FC views brings incredible difficulties to deep learning models or cardiologists. Hence, we propose a Pseudo-Siamese Feature Fusion Generative Adversarial Network (PSFFGAN), synthesizing high-quality fetal FC views using FC sketch images. In addition, we propose a novel Triplet Generative Adversarial Loss Function (TGALF), which optimizes PSFFGAN to fully extract the cardiac anatomical structure information provided by FC sketch images to synthesize the corresponding fetal FC views with speckle noises, artifacts, and other ultrasonic characteristics. The experimental results show that the fetal FC views synthesized by our proposed PSFFGAN have the best objective evaluation values: SSIM of 0.4627, MS-SSIM of 0.6224, and FID of 83.92, respectively. More importantly, two professional cardiologists evaluate healthy FC views and CHD FC views synthesized by our PSFFGAN, giving a subjective score that the average qualified rate is 82% and 79%, respectively, which further proves the effectiveness of the PSFFGAN.

引用

页码：1193 / 1204

页数：12