DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images

Understand

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 ($79.0$).