Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper, we address these issues with a framework that adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workflows in the clinical environment. To validate the architecture-independent effects of our approach we compare a neural architecture based on dilated convolutions for parallel multi-scale processing (a modified Mixed-Scale Dense Network: MS-D Net) to traditional scaling operations (a modified U-Net). Finally, we show that an ensemble model combines the strengths across different individual methods. Our framework is simple to implement into existing deep learning pipelines for CT analysis. It performs well on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness. Thus our framework is an essential step towards performing robust segmentation of unknown real-world samples.

在过去的几年里，机器学习极大地改进了医学图像分析。尽管数据驱动的方法本质上是自适应的，因此是通用的，但它们通常不会以相同的方式处理来自不同成像模式的数据。特别是计算机断层扫描 (CT) 数据对基于卷积神经网络 (CNN) 的医学图像分割提出了许多挑战，这主要是由于强度的动态范围广泛以及 CT 体积记录切片数量的变化。在本文中，我们通过一个框架来解决这些问题，该框架将特定领域的数据预处理和增强功能添加到最先进的 CNN 架构中。我们的主要重点是稳定样本的预测性能，作为临床环境中自动化和半自动化工作流程中使用的强制性要求。为了验证我们的方法的独立于架构的效果，我们将基于扩张卷积的并行多尺度处理的神经架构（修改后的混合尺度密集网络：MS-D Net）与传统缩放操作（修改后的 U-Net）进行比较。最后，我们展示了集成模型结合了不同个体方法的优势。我们的框架很容易实现到现有的深度学习管道中进行 CT 分析。它在肝脏和肾脏分割等一系列任务上表现良好，在体积大小差异很大和切片厚度不同的情况下，预测性能没有显着差异。因此，我们的框架是对未知的现实世界样本进行稳健分割的重要一步。