Due to the computational complexity of 3D medical image segmentation, training with downsampled images is a common remedy for out-of-memory errors in deep learning. Nevertheless, as standard spatial convolution is sensitive to variations in image resolution, the accuracy of a convolutional neural network trained with downsampled images can be suboptimal when applied on the original resolution. To address this limitation, we introduce FNOSeg3D, a 3D segmentation model robust to training image resolution based on the Fourier neural operator (FNO). The FNO is a deep learning framework for learning mappings between functions in partial differential equations, which has the appealing properties of zero-shot super-resolution and global receptive field. We improve the FNO by reducing its parameter requirement and enhancing its learning capability through residual connections and deep supervision, and these result in our FNOSeg3D model which is parameter efficient and resolution robust. When tested on the BraTS'19 dataset, it achieved superior robustness to training image resolution than other tested models with less than 1% of their model parameters.

翻译：由于三维医学图像分割的计算复杂性，使用降采样图像进行训练是深度学习中避免内存溢出的常见方法。然而，由于标准空间卷积对图像分辨率变化敏感，当在原始分辨率上应用时，用降采样图像训练的卷积神经网络精度可能欠佳。为解决这一局限，我们提出FNOSeg3D——一种基于傅里叶神经算子（FNO）且对训练图像分辨率鲁棒的三维分割模型。FNO是一种用于学习偏微分方程中函数映射的深度学习框架，具有零样本超分辨率和全局感受野等优良特性。我们通过减少参数需求并利用残差连接和深度监督增强学习能力对FNO进行改进，由此得到参数高效且分辨率鲁棒的FNOSeg3D模型。在BraTS'19数据集上的测试表明，该模型对训练图像分辨率的鲁棒性优于其他测试模型，且其参数量不足它们的1%。