Title: DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images,
Abstract: We present DLTK, a toolkit providing baseline implementations for efficient
experimentation with deep learning methods on biomedical images. It builds on
top of TensorFlow and its high modularity and easy-to-use examples allow for a
low-threshold access to state-of-the-art implementations for typical medical
imaging problems. A comparison of DLTK's reference implementations of popular
network architectures for image segmentation demonstrates new top performance
on the publicly available challenge data "Multi-Atlas Labeling Beyond the
Cranial Vault". The average test Dice similarity coefficient of $81.5$ exceeds
the previously best performing CNN ($75.7$) and the accuracy of the challenge
winning method ($79.0$).