In medical imaging analysis, deep learning has shown promising results. We frequently rely on volumetric data to segment medical images, necessitating the use of 3D architectures, which are commended for their capacity to capture interslice context. However, because of the 3D convolutions, max pooling, up-convolutions, and other operations utilized in these networks, these architectures are often more inefficient in terms of time and computation than their 2D equivalents. Furthermore, there are few 3D pretrained model weights, and pretraining is often difficult. We present a simple yet effective 2D method to handle 3D data while efficiently embedding the 3D knowledge during training. We propose transforming volumetric data into 2D super images and segmenting with 2D networks to solve these challenges. Our method generates a super-resolution image by stitching slices side by side in the 3D image. We expect deep neural networks to capture and learn these properties spatially despite losing depth information. This work aims to present a novel perspective when dealing with volumetric data, and we test the hypothesis using CNN and ViT networks as well as self-supervised pretraining. While attaining equal, if not superior, results to 3D networks utilizing only 2D counterparts, the model complexity is reduced by around threefold. Because volumetric data is relatively scarce, we anticipate that our approach will entice more studies, particularly in medical imaging analysis.

翻译：在医学影像分析领域，深度学习已展现出显著成果。我们常依赖体素数据来分割医学图像，这需要使用三维架构——此类架构因能捕获层间上下文信息而备受推崇。然而，由于网络中采用三维卷积、最大池化、上采样等操作，这些架构在时间和计算效率上通常低于其二维对应模型。此外，三维预训练模型权重数量稀缺且预训练过程往往困难重重。我们提出一种简单高效的二维方法处理三维数据，同时在训练过程中有效嵌入三维知识。通过将体素数据转换为二维超级图像并使用二维网络进行分割，我们解决了上述挑战。该方法将三维图像中的切片逐一拼接生成超分辨率图像，期望深度神经网络能够空间性地捕捉并学习这些特性，尽管会丢失深度信息。本研究旨在为体素数据处理提供全新视角，并采用CNN与ViT网络及自监督预训练方法验证假设。在获得与三维网络相当甚至更优结果的同时，模型复杂度降低约三倍。鉴于体素数据相对稀缺，我们期待该方法能吸引更多研究，尤其是在医学影像分析领域。