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arXiv · 1711.06853

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

Nick Pawlowski·Sofia Ira Ktena·Matthew C. H. Lee·Bernhard Kainz·Daniel Rueckert·Ben Glocker·Martin Rajchl
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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 ($79.0$).

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Nick Pawlowski, Sofia Ira Ktena, Matthew C. H. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl. 2017-11-18. DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images. https://arxiv.org/abs/1711.06853

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