Chen Lijiang, Qiao Changkun, Wu Meijing, Cai Linghan, Yin Cong, Yang Mukun, Sang Xiubo, Bai Wenpei
School of Electronic and Information Engineering, Beihang University, No. 37 Xueyuan Road, Haidian District, Beijing 100191, China.
Department of Obstetrics and Gynecology, Beijing Shijitan Hospital, Capital Medical University, Beijing 100038, China.
Bioengineering (Basel). 2023 Feb 1;10(2):184. doi: 10.3390/bioengineering10020184.
Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound images. However, clinically processed 2D ovarian-tumor ultrasound images contain many artificially recognized symbols, such as fingers, crosses, dashed lines, and letters which assist artificial intelligence (AI) in image recognition. These symbols are widely distributed within the lesion's boundary, which can also affect the useful feature-extraction-utilizing networks and thus decrease the accuracy of lesion classification and segmentation. Image inpainting techniques are used for noise and object elimination from images. To solve this problem, we observed the MMOTU dataset and built a 2D ovarian-tumor ultrasound image inpainting dataset by finely annotating the various symbols in the images. A novel framework called mask-guided generative adversarial network (MGGAN) is presented in this paper for 2D ovarian-tumor ultrasound images to remove various symbols from the images. The MGGAN performs to a high standard in corrupted regions by using an attention mechanism in the generator to pay more attention to valid information and ignore symbol information, making lesion boundaries more realistic. Moreover, fast Fourier convolutions (FFCs) and residual networks are used to increase the global field of perception; thus, our model can be applied to high-resolution ultrasound images. The greatest benefit of this algorithm is that it achieves pixel-level inpainting of distorted regions without clean images. Compared with other models, our model achieveed better results with only one stage in terms of objective and subjective evaluations. Our model obtained the best results for 256 × 256 and 512 × 512 resolutions. At a resolution of 256 × 256, our model achieved for SSIM, 22.66 for FID, and 0.07806 for LPIPS. At a resolution of 512 × 512, our model achieved 0.9208 for SSIM, 25.52 for FID, and 0.08300 for LPIPS. Our method can considerably improve the accuracy of computerized ovarian tumor diagnosis. The segmentation accuracy was improved from 71.51% to 76.06% for the Unet model and from 61.13% to 66.65% for the PSPnet model in clean images.
二维卵巢肿瘤超声图像的质量会对诊断结果产生根本性影响。然而，临床处理后的二维卵巢肿瘤超声图像包含许多人工识别的符号，如手指、十字、虚线和字母，这些有助于人工智能（AI）进行图像识别。这些符号广泛分布在病变边界内，这也会影响利用特征提取的网络，从而降低病变分类和分割的准确性。图像修复技术用于去除图像中的噪声和物体。为了解决这个问题，我们观察了MMOTU数据集，并通过对图像中的各种符号进行精细标注，构建了一个二维卵巢肿瘤超声图像修复数据集。本文提出了一种名为掩码引导生成对抗网络（MGGAN）的新颖框架，用于二维卵巢肿瘤超声图像，以去除图像中的各种符号。MGGAN通过在生成器中使用注意力机制，在受损区域表现出高标准，更加关注有效信息并忽略符号信息，使病变边界更加逼真。此外，快速傅里叶卷积（FFC）和残差网络用于扩大全局感知范围；因此，我们的模型可以应用于高分辨率超声图像。该算法的最大优点是它无需干净图像就能实现对失真区域的像素级修复。与其他模型相比，在客观和主观评估方面，我们的模型仅通过一个阶段就取得了更好的结果。我们的模型在256×256和512×512分辨率下取得了最佳结果。在256×256分辨率下，我们的模型SSIM为 ，FID为22.66，LPIPS为0.07806。在512×512分辨率下，我们的模型SSIM为0.9208，FID为25.52,LPIPS为0.08300。我们的方法可以显著提高计算机辅助卵巢肿瘤诊断的准确性。在干净图像中，Unet模型的分割准确率从71.51%提高到76.06%，PSPnet模型的分割准确率从61.13%提高到66.65%。
