Computer-aided diagnosis is pivotal in augmenting the diagnostic efficiency of ultrasound images. Nonetheless, the substantial presence of noise and artifacts in ultrasound images presents a challenge to the precise segmentation of the target region. To highlight and analyze the diagnostic information such as tissues, organs, and lesions in ultrasound medical images more accurately, a target region extraction method combining the improved PRIDNet and UCTransNet for ultrasound medical images is proposed. The method sequentially enhances and segments the target region of the image, thereby solving the problem of inconspicuous target region caused by noise and artifacts in ultrasound images. There are three key characteristics: (i) The feature extraction part of the enhancement network (improved PRIDNet) is redesigned for speckle noise to improve the network's ability to extract information and highlight the feature information of the target region in ultrasound images. (ii) The segmentation network with the addition of the underlying information on UCTransNet would effectively improves Channel-wise Cross fusion Transformer (CCT) and decoder feature fusion capability. (iii) By combining the enhancement network with the segmentation network, we can further improve the segmentation accuracy of the target region in the presence of noise interference. The experiments conducted on both UFSU that we prepared and on some public datasets including BUSI, FHC, and CT2US have demonstrated that The proposed method attains MIoU, DSC, Acc, and HD of 96.34%, 98.12%, 99.35%, and 8.64 in CT2US, respectively. The method significantly surpasses those certain state-of-the-art methods, demonstrating its potential to offer valuable guidance for clinical treatment. The code will be publicly released at https://github.com/425877/Target-Region-Extraction-Method-for-Ultrasound-Medical-Images.

中文翻译：

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一种基于改进PRIDNet和UCTransNet的超声医学图像目标区域提取方法

计算机辅助诊断对于提高超声图像的诊断效率至关重要。尽管如此，超声图像中大量存在的噪声和伪影对目标区域的精确分割提出了挑战。为了更准确地突出和分析超声医学图像中的组织、器官和病灶等诊断信息，提出一种结合改进的PRIDNet和UCTransNet的超声医学图像目标区域提取方法。该方法对图像的目标区域进行依次增强和分割，解决了超声图像中噪声和伪影导致的目标区域不明显的问题。具有三个关键特征：（i）增强网络（改进的 PRIDNet）的特征提取部分针对散斑噪声进行了重新设计，以提高网络提取信息的能力并突出超声图像中目标区域的特征信息。 (ii)在UCTransNet上添加底层信息的分割网络将有效提高Channel-wise Cross fusion Transformer (CCT)和解码器特征融合能力。 (iii)通过将增强网络与分割网络相结合，可以进一步提高存在噪声干扰的目标区域的分割精度。在我们准备的 UFSU 和一些公共数据集（包括 BUSI、FHC 和 CT2US）上进行的实验表明，该方法在 CT2US 中的 MIoU、DSC、Acc 和 HD 分别为 96.34%、98.12%、99.35% 和 8.64 ， 分别。该方法显着超越了某些最先进的方法，证明了其为临床治疗提供有价值的指导的潜力。该代码将在 https://github.com/425877/Target-Region-Extraction-Method-for-Ultrasound-Medical-Images 上公开发布。