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

By Nick Pawlowski, Sofia Ira Ktena, M. J. Lee, Bernhard Kainz, Daniel Rueckert, Ben Glocker, Martin Rajchl. 2017

85 citations recorded by the source corpus.

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 ($79.0$).

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

An overview of deep learning in medical imaging focusing on MRI (2018, methodology): “An open source convolutional neural networks platform for medical image analysis and image-guided therapy [320,321] http://niftynet.io DLTK. State of the art reference implementations for deep learning on medical images [131] https://github.com/DLTK/DLTK DeepMedic [322] https://github.com/Kamnitsask”
HyperDense-Net: A Hyper-Densely Connected CNN for Multi-Modal Image Segmentation (2018, unknown): “The implementation is provided in [53].”
A Gentle Introduction to Deep Learning in Medical Image Processing (2018, background): “tutorials and reviews [9, 10, 11, 12, 13, 14, 15, 16, 17, 18] over blog posts and jupyter notebooks [19, 20, 21] to entire books [22, 23].”
MONAI: An open-source framework for deep learning in healthcare (2022, background): “With the increasing popularity of AI in healthcare, several open-source healthcare-speciﬁc frameworks have also been developed by academic groups (NiftyNet [8], DLTK [9], DeepNeuro [10]) and industry teams (NVIDIA Clara, Microsoft Project InnerEye).”
TorchIO: A Python library for efficient loading, preprocessing, augmentation and patch-based sampling of medical images in deep learning (2020, methodology): “Open-source frameworks for training CNNs with medical images have been built on top of TensorFlow [5, 6, 7].”