An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep

An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning论文翻译
摘要：
Abstract. This paper proposes an efficient solution for tumor segmentation and classification in breast ultrasound (BUS) images. We propose to add an atrous convolution layer to the conditional generative adversarial network (cGAN) segmentation model to learn tumor features at different resolutions of BUS images. To automatically re-balance the relative impact of each of the highest level encoded features, we also propose to add a channel-wise weighting block in the network. In addition, the SSIM and L1-norm loss with the typical adversarial loss are used as a loss function to train the model. Our model outperforms the state-of-the-art segmentation models in terms of the Dice and IoU metrics, achieving top scores of 93.76% and 88.82%, respectively. In the classification stage, we show that few statistics features extracted from the shape of the boundaries of the predicted masks can properly discriminate between benign and malignant tumors with an accuracy of 85%.

本文提出了一种有效的乳腺超声（BUS）图像肿瘤分割和分类解决方案。 我们提出在条件生成对抗网络（cGAN）分割模型中添加一个空洞卷积层，以便在BUS图像的不同分辨率下学习肿瘤特征。 为了自动重新平衡每个最高级别编码特征的相对影响，我们还建议在网络中添加频道方向加权块。 此外，具有典型对抗性损失的SSIM和L1范数损失被用作训练模型的损失函数。 我们的模型在Dice和IoU指标方面优于最先进的细分模型，分别达到93.76％和88.82％的最高分。 在分类阶段，我们表明从预测掩模的边界形状中提取的很少的统计特征可以恰当地区分良性和恶性肿瘤，准确度为85％。

1 Introduction
Breast cancer is one of the most commonly diagnosed causes of death in women worldwide [14]. Screening with mammography can recognize tumors in the early stages. Despite, some breast cancers may not be captured in mammographies (e.g., in the case of dense breasts). Ultrasound has been recommended as a powerful adjunct screening tool for detecting breast cancers that may be occluded in mammographies [8]. Computer-aided diagnosis (CAD) systems are widely used to detect, segment and classify masses in breast ultrasound (BUS) images. One of the main steps of BUS CAD systems is tumor segmentation。
乳腺癌是全世界女性最常被诊断的死因之一[14]。 使用乳房X线照相术进行筛查可以识别早期阶段的肿瘤。 尽管如此，一些乳腺癌可能不会在乳房X线照相术中被捕获（例如，在乳房密集的情况下）。 超声波被推荐作为一种强大的辅助筛查工具，用于检测可能在乳房X线照相术中被遮挡的乳腺癌[8]。 计算机辅助诊断（CAD）系统广泛用于检测，分割和分类乳房超声（BUS）图像中的质量。 BUS CAD系统的主要步骤之一是肿瘤分割。
Over the last two decades, several BUS image segmentation methods have been proposed, which can be categorized into semi-automated and fully automated according to the degree of human intervention. In [1], a region growing based algorithm was used to automatically extract the regions that contain the tumors, and image super-resolution and texture analysis methods were used to discriminate benign tumors from the malignant ones. Recently, some deep learning based models have been proposed to improve the performance of breast tumor segmentation methods. In [16], two convolutional neural network (CNN) architectures have been used to segment BUS images into the skin, mass, fibro-glandular, and fatty tissues (an accuracy of 90%). Hu et al [5] combined a dilated fully convolutional network with a phase-based active contour model to segment breast tumors, achieving dice score of 88.97%.
在过去的二十年中