When using convolutional neural networks (CNNs) for segmentation of organs and lesions in medical images, the conventional approach is to work with inputs and outputs either as single slice [two‐dimensional (2D)] or whole volumes [three‐dimensional (3D)]. One common alternative, in this study denoted as pseudo‐3D, is to use a stack of adjacent slices as input and produce a prediction for at least the central slice. This approach gives the network the possibility to capture 3D spatial information, with only a minor additional computational cost.

中文翻译：

---

用于医学图像分割的卷积神经网络的多切片输入评估

当使用卷积神经网络 (CNN) 对医学图像中的器官和病变进行分割时，传统方法是将输入和输出作为单切片 [二维 (2D)] 或整个体积 [三维 (3D)] 进行处理]。一种常见的替代方案（在本研究中称为伪 3D）是使用一堆相邻切片作为输入，并至少对中心切片进行预测。这种方法使网络能够捕获 3D 空间信息，而只需少量的额外计算成本。