Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:3878-3881. doi: 10.1109/EMBC48229.2022.9871091.
Automatic lesion segmentation in breast ultrasound (BUS) images aids in the diagnosis of breast cancer, the most common type of cancer in women. Accurate lesion segmentation in ultrasound images is a challenging task due to speckle noise, artifacts, shadows, and lesion variability in size and shape. Recently, convolutional neural networks have demonstrated impressive results in medical image segmentation tasks. However, the lack of public benchmarks and a standardized evaluation method hampers the networks' performance comparison. This work presents a benchmark of seven state-of-the-art methods for the automatic breast lesion segmentation task. The methods were evaluated on a multi-center BUS dataset composed of three public datasets. Specifically, the U-Net, Dynamic U-Net, Semantic Segmentation Deep Residual Network with Variational Autoencoder (SegResNetVAE), U-Net Transformers, Residual Feedback Network, Multiscale Dual Attention-Based Network, and Global Guidance Network (GG-Net) architectures were evaluated. The training was performed with a combination of the cross-entropy and Dice loss functions and the overall performance of the networks was assessed using the Dice coefficient, Jaccard index, accuracy, recall, specificity, and precision. Despite all networks having obtained Dice scores superior to 75%, the GG-Net and SegResNetVAE architectures outperform the remaining methods, achieving 82.56% and 81.90%, respectively. Clinical Relevance- The results of this study allowed to prove the potential of deep neural networks to be used in clinical practice for breast lesion segmentation also suggesting the best model choices.
自动乳腺超声（BUS）图像病变分割有助于乳腺癌的诊断，乳腺癌是女性最常见的癌症类型。由于斑点噪声、伪影、阴影以及病变大小和形状的可变性，超声图像中的准确病变分割是一项具有挑战性的任务。最近，卷积神经网络在医学图像分割任务中取得了令人印象深刻的结果。然而，缺乏公共基准和标准化的评估方法阻碍了网络性能的比较。本工作提出了一个用于自动乳腺病变分割任务的七个最先进方法的基准。这些方法在由三个公共数据集组成的多中心 BUS 数据集上进行了评估。具体来说，评估了 U-Net、Dynamic U-Net、具有变分自动编码器的语义分割深度残差网络（SegResNetVAE）、U-Net 转换器、残差反馈网络、多尺度双注意力网络和全局引导网络（GG-Net）架构。使用交叉熵和 Dice 损失函数的组合进行了训练，并使用 Dice 系数、Jaccard 指数、准确性、召回率、特异性和精度来评估网络的整体性能。尽管所有网络的 Dice 评分均高于 75%，但 GG-Net 和 SegResNetVAE 架构的表现优于其他方法，分别达到 82.56%和 81.90%。临床相关性-本研究的结果证明了深度学习神经网络在临床实践中用于乳腺病变分割的潜力，同时也提出了最佳模型选择。
