Magnetic resonance imaging (MRI) data is heterogeneous due to differences in device manufacturers, scanning protocols, and inter-subject variability. A conventional way to mitigate MR image heterogeneity is to apply preprocessing transformations such as anatomy alignment, voxel resampling, signal intensity equalization, image denoising, and localization of regions of interest. Although a preprocessing pipeline standardizes image appearance, its influence on the quality of image segmentation and on other downstream tasks in deep neural networks has never been rigorously studied. We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in intra- and inter-dataset training scenarios. Our results demonstrate that most popular standardization steps add no value to the network performance; moreover, preprocessing can hamper model performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization. Finally, we show that the contribution of skull-stripping in data preprocessing is almost negligible if measured in terms of estimated tumor volume. We show that the only essential transformation for accurate deep learning analysis is the unification of voxel spacing across the dataset. In contrast, inter-subjects anatomy alignment in the form of non-rigid atlas registration is not necessary and intensity equalization steps (denoising, bias-field correction and histogram matching) do not improve models' performance. The study code is accessible online \footnote{https://github.com/MedImAIR/brain-mri-processing-pipeline}.

翻译：磁共振成像（MRI）数据因设备制造商、扫描协议及受试者间差异而呈现异质性。缓解MR图像异质性的常规方法包括应用解剖对齐、体素重采样、信号强度均衡、图像去噪及感兴趣区域定位等预处理变换。尽管预处理流程可标准化图像外观，但其对深度神经网络中图像分割质量及后续任务的影响从未得到严格研究。我们在三个公开数据集上开展实验，评估了数据集内与跨数据集训练场景中不同预处理步骤的效果。结果表明，大多数标准化步骤对网络性能无增益；此外，预处理甚至可能削弱模型性能。我们提出，图像强度归一化方法未提升模型准确性，原因在于图像标准化降低了信号方差。最后，我们发现若以估算肿瘤体积为衡量标准，数据预处理中颅骨剥离的贡献几乎可忽略。研究表明，深度学习精准分析唯一不可或缺的变换是统一数据集中的体素间距。相反，以非刚性图谱配准形式实现的受试者间解剖对齐并非必要，而强度均衡步骤（去噪、偏场校正及直方图匹配）亦未改善模型性能。研究代码公开访问于\footnote{https://github.com/MedImAIR/brain-mri-processing-pipeline}。