Ultrasonic breast tumor extraction based on adversarial mechanism and active contour. (October 2022) - Wang, Jinhong; Chen, Guiqing; Chen, Shiqiang; Joseph Raj, Alex Noel; Zhuang, Zhemin; Xie, Lei; Ma, Shuhua
Highlights: Propose an ultrasonic breast tumor segmentation network combining antagonism mechanism and contour optimization. Using active contour modules to highlight ultrasound tumor edges, forming a bi-directional optimization with a Deformed U-Net. Antagonism mechanism is adopted to improve the precision of segmentation network. The loss of discriminator, Deformed U-net and active contour module is used for collaborative optimization of the whole network. The result of this method is better than that of other segmentation networks of breast lesions. Abstract: Background and objective: Breast cancer is a high incidence of gynecological diseases; breast ultrasound screening can effectively reduce the mortality rate of breast cancer. In breast ultrasound images, the localization and segmentation of tumor lesions are important steps for the extraction of lesions, which helps clinicians evaluate breast lesions quantitatively and makes better clinical diagnosis of the disease. However, the segmentation of breast lesions is difficult due to the blurred and uneven edges of some lesions. In this paper, we propose a segmentation framework combining active contour module and deep learning adversarial mechanism and apply it for the segmentation of breast tumor lesions. Method: We use a conditional adversarial network as the main framework. The generator is a segmentation network consisting of a Deformed U-Net and an active contour module. Here, the Deformed U-Net performs pixel-level segmentation for breast ultrasound images. The active contour module refines the tumor lesion edges, and the refined result provides loss information for Deformed U-Net. Therefore, the Deformed U-Net can better classify the edge pixels. The discriminator is the Markov discriminator; this discriminator provides loss feedback for the segmentation network. We cross-train the discriminator and segmentation network to implement Adversarial Mechanism for getting a more optimized segmentation network. Results: The segmentation performance of the segmentation network for breast ultrasound images is improved by adding a Markov discriminator to provide discriminant loss training. The proposed method for segmenting the tumor lesions in breast ultrasound image obtains dice coefficient: 89.7%, accuracy: 98.1%, precision: 86.3%, mean-intersection-over-union: 82.2%, recall: 94.7%, specificity: 98.5% and F1score: 89.7%. Conclusion: Comparing with traditional methods, the proposed method gives better performance. The experimental results show that the proposed method can effectively segment the lesions in breast ultrasound images, and then assist doctors to realize the diagnosis of breast lesions.