  Fueled by recent advances in machine learning, there has been tremendous
progress in the field of semantic segmentation for the medical image computing
community. However, developed algorithms are often optimized and validated by
hand based on one task only. In combination with small datasets, interpreting
the generalizability of the results is often difficult. The Medical
Segmentation Decathlon challenge addresses this problem, and aims to facilitate
development of generalizable 3D semantic segmentation algorithms that require
no manual parametrization. Such an algorithm was developed and is presented in
this paper. It consists of a 3D convolutional neural network with
encoder-decoder architecture employing residual-connections, skip-connections
and multi-level generation of predictions. It works on anisotropic
voxel-geometries and has anisotropic depth, i.e., the number of downsampling
steps is a task-specific parameter. These depths are automatically inferred for
each task prior to training. By combining this flexible architecture with
on-the-fly data augmentation and little-to-no pre-- or postprocessing,
promising results could be achieved. The code developed for this challenge will
be available online after the final deadline at:
https://github.com/ORippler/MSD_2018
