WATUNet：一种用于体积扫描成像超声分割的深度神经网络
WATUNet: A Deep Neural Network for Segmentation of Volumetric Sweep Imaging Ultrasound
AI总结：
针对超声乳腺癌诊断依赖专业技师且传统UNet存在梯度消失等局限的问题，提出WATUNet模型，通过在编解码器间引入小波门和注意力门提升分割性能，在公开和VSI数据集上取得优异结果。
摘要：
目的。全球乳腺癌诊断的获取受限导致治疗延误。超声是一种有效但未被充分利用的方法，需要超声技师接受专门培训，这阻碍了其广泛应用。方法。体积扫描成像（VSI）是一种创新方法，使未经培训的操作员能够捕获高质量超声图像。与卷积神经网络（CNNs）等深度学习相结合，它有望改变乳腺癌诊断，提高准确性，节省时间和成本，并改善患者预后。广泛使用的UNet架构以医学图像分割闻名，但存在梯度消失、缺乏多尺度特征提取和选择性区域注意力等局限性。在本研究中，我们提出了一种名为Wavelet_Attention_UNet（WATUNet）的新型分割模型。在该模型中，我们在编码器和解码器之间加入了小波门（WGs）和注意力门（AGs），而不是简单的连接，以克服上述局限，从而提高模型性能。主要结果。分析使用了两个数据集。包含780张图像的公开“乳腺超声图像”（BUSI）数据集和包含3818张图像的VSI数据集。两个数据集均包含分为三类的分割病灶：无肿块、良性肿块和恶性肿块。我们的分割结果显示，与其他深度网络相比具有优越性能。所提出的算法在VSI数据集上获得了0.94的Dice系数和0.94的F1分数，在公开数据集上分别得分为0.93和0.94。
英文摘要：
Objective. Limited access to breast cancer diagnosis globally leads to delayed treatment. Ultrasound, an effective yet underutilized method, requires specialized training for sonographers, which hinders its widespread use. Approach. Volume sweep imaging (VSI) is an innovative approach that enables untrained operators to capture high-quality ultrasound images. Combined with deep learning, like convolutional neural networks (CNNs), it can potentially transform breast cancer diagnosis, enhancing accuracy, saving time and costs, and improving patient outcomes. The widely used UNet architecture, known for medical image segmentation, has limitations, such as vanishing gradients and a lack of multi-scale feature extraction and selective region attention. In this study, we present a novel segmentation model known as Wavelet_Attention_UNet (WATUNet). In this model, we incorporate wavelet gates (WGs) and attention gates (AGs) between the encoder and decoder instead of a simple connection to overcome the limitations mentioned, thereby improving model performance. Main results. Two datasets are utilized for the analysis. The public "Breast Ultrasound Images" (BUSI) dataset of 780 images and a VSI dataset of 3818 images. Both datasets contained segmented lesions categorized into three types: no mass, benign mass, and malignant mass. Our segmentation results show superior performance compared to other deep networks. The proposed algorithm attained a Dice coefficient of 0.94 and an F1 score of 0.94 on the VSI dataset and scored 0.93 and 0.94 on the public dataset, respectively.
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