Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting

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...

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Bibliographic Details
Published in:	Bioengineering (Basel) Vol. 10; no. 2; p. 184
Main Authors:	Chen, Lijiang, Qiao, Changkun, Wu, Meijing, Cai, Linghan, Yin, Cong, Yang, Mukun, Sang, Xiubo, Bai, Wenpei
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 01-02-2023 MDPI
Subjects:	2D ultrasound image Accuracy Algorithms Artificial intelligence attention mechanism Bioengineering Classification Clinical medicine Datasets Deep learning Experiments Feature extraction GAN Generative adversarial networks Image classification image inpainting Image processing Image quality Image resolution Image segmentation lesion segmentation Lesions Medical imaging Medical research Methods Object recognition ovarian tumor Ovaries Semantics Symbols Thyroid gland Tumors Ultrasonic imaging Ultrasound United States deep learning GAN attention mechanism image inpainting lesion segmentation 2D ultrasound image medical image analysis ovarian tumor
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Summary:	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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 These authors contributed equally to this work.
ISSN:	2306-5354 2306-5354
DOI:	10.3390/bioengineering10020184