  Many recent medical segmentation systems rely on powerful deep learning
models to solve highly specific tasks. To maximize performance, it is standard
practice to evaluate numerous pipelines with varying model topologies,
optimization parameters, pre- & postprocessing steps, and even model cascades.
It is often not clear how the resulting pipeline transfers to different tasks.
We propose a simple and thoroughly evaluated deep learning framework for
segmentation of arbitrary medical image volumes. The system requires no
task-specific information, no human interaction and is based on a fixed model
topology and a fixed hyperparameter set, eliminating the process of model
selection and its inherent tendency to cause method-level over-fitting. The
system is available in open source and does not require deep learning expertise
to use. Without task-specific modifications, the system performed better than
or similar to highly specialized deep learning methods across 3 separate
segmentation tasks. In addition, it ranked 5-th and 6-th in the first and
second round of the 2018 Medical Segmentation Decathlon comprising another 10
tasks. The system relies on multi-planar data augmentation which facilitates
the application of a single 2D architecture based on the familiar U-Net.
Multi-planar training combines the parameter efficiency of a 2D fully
convolutional neural network with a systematic train- and test-time
augmentation scheme, which allows the 2D model to learn a representation of the
3D image volume that fosters generalization.
