3-D radiotherapy is an effective treatment modality for breast cancer. In 3-D radiotherapy, delineation of the clinical target volume (CTV) is an essential step in the establishment of treatment plans. However, manual delineation is subjective and time consuming. In this study, we propose an automated segmentation model based on deep neural networks for the breast cancer CTV in planning computed tomography (CT). Our model is composed of three stages that work in a cascade manner, making it applicable to real-world scenarios. The first stage determines which slices contain CTVs, as not all CT slices include breast lesions. The second stage detects the region of the human body in an entire CT slice, eliminating boundary areas, which may have side effects for the segmentation of the CTV. The third stage delineates the CTV. To permit the network to focus on the breast mass in the slice, a novel dynamically strided convolution operation, which shows better performance than standard convolution, is proposed. To train and evaluate the model, a large dataset containing 455 cases and 50 425 CT slices is constructed. The proposed model achieves an average dice similarity coefficient (DSC) of 0.802 and 0.801 for right-0 and left-sided breast, respectively. Our method shows superior performance to that of previous state-of-the-art approaches.

中文翻译：

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使用深度神经网络自动分割乳腺癌计划 CT 中的临床目标体积


3D放射治疗是乳腺癌的有效治疗方式。在 3D 放射治疗中，描绘临床靶区 (CTV) 是制定治疗计划的重要步骤。然而，手动描绘是主观的并且耗时。在这项研究中，我们提出了一种基于深度神经网络的自动分割模型，用于计划计算机断层扫描 (CT) 中的乳腺癌 CTV。我们的模型由以级联方式工作的三个阶段组成，使其适用于现实场景。第一阶段确定哪些切片包含 CTV，因为并非所有 CT 切片都包含乳腺病变。第二阶段检测整个CT切片中的人体区域，消除边界区域，这可能对CTV的分割产生副作用。第三阶段描绘了CTV。为了使网络能够专注于切片中的乳房肿块，提出了一种新颖的动态跨步卷积运算，该运算表现出比标准卷积更好的性能。为了训练和评估模型，构建了包含 455 个病例和 50 425 个 CT 切片的大型数据集。该模型对于右侧 0 乳房和左侧乳房的平均骰子相似系数 (DSC) 分别为 0.802 和 0.801。我们的方法显示出比以前最先进的方法更优越的性能。