The UNet has become the golden standard method for the segmentation of 2D medical images that any new method must be validated against. In recent years, a number of variations to the seminal UNet have been proposed with promising results in the papers introducing them. However, there is no clear consensus if any of these architectures generalize as well and the UNet currently remains the methodological golden standard. For the segmentation of 3D scans, UNet-inspired methods are also dominant, but there is a larger variety across applications. By evaluating the architectures in a different dimensionality, embedded in a different method, and for a different task, we aimed to evaluate if any of these UNet alternatives are promising as a new golden standard that generalizes even better than the UNet. The purpose of this study was to compare UNet inspired models for generalized 3D segmentation. To efficiently segment the 3D scans, we employed each UNet variant architecture as the central 2D segmentation core in the multi-planar UNet 3D segmentation method that previously demonstrated excellent generalization in the MICCAI Segmentation Decathlon. It would strongly support a claim of generalizability, if a promising UNet-variant consistently outperforms the UNet in this quite different setting. The experimental results show that the multi-planar-based UNet2+ (MPUNet2+) method outperforms other variants including the original multi-planar UNet (MPUNet).